1 Raw data

data <- read.csv("/home/bambrozi/2_CORTISOL/Data/T4_Elora_Data_04_25_2024.csv")

# Replace "treated" with NA
data$T4Cortisol[data$T4Cortisol == "treated" | data$T4Cortisol == "Treated at T2" | data$T4Cortisol == "treated at T2"] <- NA
# Convert the column to numeric, coercing non-numeric values to NA
data$T4Cortisol <- as.numeric(as.character(data$T4Cortisol))
#Filtering only the lines with values
data <- data[!is.na(data$T4Cortisol),]
#creating new data file cleaned  
write.csv(data, "/home/bambrozi/2_CORTISOL/Data/data_clean.csv", row.names = F)

print(data)

2 Continuous Phenotype

# Summary Statistics
summary(data$T4Cortisol)
# Histogram
hist(data$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")
# Boxplot
boxplot(data$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")
# Density Plot
plot(density(data$T4Cortisol), main = "Density Plot of T4 Cortisol", xlab = "T4 Cortisol", ylab = "Density")
# Calculate the theoretical quantiles
qqnorm(data$T4Cortisol, main = "QQ Plot of T4Cortisol", xlim = c(min(qqnorm(data$T4Cortisol)$x), max(qqnorm(data$T4Cortisol)$x)), ylim = c(min(qqnorm(data$T4Cortisol)$y), max(qqnorm(data$T4Cortisol)$y) + 2 * IQR(qqnorm(data$T4Cortisol)$y)))
# Add the QQ line
qqline(data$T4Cortisol, col = "red")

Summary statistics My Image

Histogram My Image

Density My Image

Box_Plot My Image

qq_Plot My Image

Shapiro-Wilk normality test My Image

2.1 Categorical Phenotype

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

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data$Categorical <- ifelse(data$T4Cortisol >= 956, "H", 
                           ifelse(data$T4Cortisol <= 190.8, "L", "M"))

table(data$Categorical)
library(ggplot2)

# Reorder the levels of the 'Categorical' column
data$Categorical <- factor(data$Categorical, levels = c("L", "M", "H"))

# Create the histogram with reordered categories
ggplot(data, aes(x = Categorical, fill = Categorical)) +
  geom_bar() +
  labs(title = "Histogram of T4Cortisol by Category",
       x = "Category",
       y = "Frequency") +
  theme_minimal()

# Create the histogram
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_histogram(binwidth = 50, color = "black", alpha = 0.7) + # Adjust binwidth as needed
  labs(title = "Histogram of T4Cortisol with Color by Category",
       x = "T4 Cortisol",
       y = "Frequency",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create the density plot
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_density(alpha = 0.3) +
  labs(title = "Density Plot of T4Cortisol with Color by Category",
       x = "T4Cortisol",
       y = "Density",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create a density plot
ggplot(data, aes(x = T4Cortisol)) +
  geom_density() +
  geom_vline(xintercept = c(956, 190.8), linetype = "dashed", color = "red") +
  labs(title = "Density Plot of T4Cortisol with Vertical Lines",
       x = "T4Cortisol",
       y = "Density") +
  theme_minimal()

The animals were sorted in these three categories >H = Hight >M = Medium >L = Low

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The individuals frequency distribution in theese categories are shown in the plots below

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2.2 Removing “outliers”

Observing the previous plots I tried to remove the “outliers” phenotypes above 1250, but the outcome from Shapiro test is still indicating no normality of the data.

library(tidyverse)

data_no_out <- filter(data, T4Cortisol < 1250)

# Create QQ plot
qqnorm(data_no_out$T4Cortisol, main = "QQ Plot of T4Cortisol", xlab = "Theoretical Quantiles", ylab = "Sample Quantiles")
qqline(data$T4Cortisol, col = "red")

boxplot(data_no_out$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")

hist(data_no_out$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")

shapiro.test(data$T4Cortisol)

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3 GENOTYPES

Lucas Alcântara sent me the path to the genotype and pedigree files: /data/cgil/daiclu/6_Data/database/public_output/bruno

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In this folder we found the following files:

bruno_gntps.txt: the file with the genotypes in 012 format
bruno_ids.csv: file with elora_id, cdn_id, iid
bruno_ped.id.csv: pedigree with cdn_id
bruno_ped.iid.csv: pedigree with iid

I made a copy of this files in a folder called Raw_files:

/home/bambrozi/2_CORTISOL/RawFiles

This directory has two sub-directories:

Genotypes
Pedigree

3.1 Transforming the Genotypes in 012 format to plink format

To perfome this I used two codes

Add “column names”

sed -i '1i id Call' genotypes.txt

Recode in python

filename = 'bruno_gntps.txt'

outputFileOpen = open('genoplink.ped','w')


recode = {'0':['C','C'] , '1':['A','C'] , '2':['A','A'] , '5':['0','0'] }
for line in open(filename,'r'):
    if 'Call' in line : continue

    ids, call = line.strip().split()
    genotypes = [ recode[geno012][0] +' '+ recode[geno012][1] for geno012 in call ]

    outputFileOpen.write("%s %s %s %s %s %s %s\n" % ('HO', ids, '0','0','0','-9',' '.join(genotypes)) )

Now I have a folder called /home/bambrozi/2_CORTISOL/Geno_files with the file named genoplink.ped

4 GWAS

4.1 Checking with Phenotyped Animals also have Genotype

library(data.table)

pheno <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")
ped <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv")
geno <- fread("/home/bambrozi/2_CORTISOL/Geno_files/genoplink.ped")
geno <- geno[,c("V2")]

#Bringing cdn_id to my phenotype file
#Generate a index with the match
matching_indices <- match(pheno$ID, ped$elora_id)
# Then, assign 'cdn_id' from 'ped' to 'pheno' where there are matches
pheno$cdn_id <- ifelse(!is.na(matching_indices), ped$cdn_id[matching_indices], NA)

#Making a phenotype file only with genotyped animals
pheno_genotyped <- pheno[pheno$cdn_id %in% geno$V2,] 

#check if all animals in this file are genotyped
checkk <- pheno_genotyped$cdn_id %in% geno$V2
sum(checkk)

4.2 Generating a Phenotype file

The phenotype file should have three columns: FID, Animal_id, Phenotype

HO <- rep("HO", 252)

pheno_gwas <- as.data.frame(cbind(HO, pheno_genotyped$cdn_id, pheno_genotyped$T4Cortisol))

colnames(pheno_gwas) <- c("FID", "cdn_id", "cortisol")

pheno_gwas$cdn_id <-  as.numeric(pheno_gwas$cdn_id)
pheno_gwas$cortisol <- round(as.numeric(pheno_gwas$cortisol),2)

write.table(pheno_gwas, "/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", quote = F, row.names = F, col.names = T)

4.3 Adjusting the SNP_map to .map

map <- fread("/data/cgil/daiclu/6_Data/database/public_output/bruno/DGVsnpinfo.2404.ho")
morgan <- data.frame(X0 = rep(0, 45101))
mapa=as.data.frame(cbind(map$chr, map$snp_name, morgan$X0, map$location))
head(mapa)
write.table(x = mapa, file = "/home/bambrozi/2_CORTISOL/Geno_files/genoplink.map", row.names = FALSE, col.names = FALSE, sep = "\t", quote = FALSE)

4.4 Generating the bfiles

system("/home/local/bin/plink --cow --nonfounders --allow-no-sex --keep-allele-order --file /home/bambrozi/2_CORTISOL/Geno_files/genoplink --make-bed --out /home/bambrozi/2_CORTISOL/Geno_files/genoplink")

With the code above I generated the bfiles:

genoplink.bim
genoplink.fam
genoplink.log
genoplink.nosex

5 Quality Control

We ran the code below to perfom the QC

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files/genoplink
RESULT=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean

/home/local/bin/plink \
    --bfile ${DATA} \
    --cow \
    --allow-no-sex \
    --hwe 1e-5 \
    --maf 0.01 \
    --geno 0.1 \
    --mind 0.1 \
    --keep-allele-order \
    --make-bed \
    --out ${RESULT}

The server screen outcome is shown below. My Image

After the Quality Control we ended up with

252 samples
42,278 variants (SNPs)

6 KING

To check for duplicated individuals I performed the KINSHIP analysis using one script from Larissa Braga. Running the King Analysis on Plink.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/ONLY_GRASSHILL_AND_PHENO_after_QC/only_grasshill_and_pheno_clean
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/KING/result_king

plink2 \
    --bfile ${DATA} \
    --chr-set 29 \
    --make-king-table \
    --out ${RESULT}

This is the output screen on terminal:

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The table below is the output /home/bambrozi/2_CORTISOL/Geno_files_after_KING/result_king.kin0 and have pairwise comparisons between all individuals.

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Now we should open in R and check for individuals with more than 0,354, to perform this we can use the code below, also provided by Larissa Braga:

setwd("/home/bambrozi/2_CORTISOL/Geno_files_after_KING")

relatedness="result_king.kin0" ## change accordingly!!

library(data.table)

print(relatedness)
rel=fread(relatedness, h = T)
head(rel)

print("Individuals with different identifications above the cut off of 0.354:")
dup=subset(rel, KINSHIP >= 0.354  & IID1!=IID2)
print(dup)
nrow(dup)

So the code above will provide this output if there is any duplicated individual.

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We do not have any duplicated individual

So the file to be used are those in the directory /home/bambrozi/2_CORTISOL/Geno_files_after_QC

files:genoplink_clean

After Quality Control we didn’t lost any animal, so we don’t need to update our phenotype file

7 PCA

Now before performing the PCA analysis we need to change the FID for those individuals that has phenotype = 1 for Nadia.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/imput_pca
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/pca_result

plink \
    --bfile ${DATA} \
    --keep-allele-order \
    --chr-set 29 \
    --pca \
    --out ${RESULT}

The PCA output:

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7.1 PCA Plot

After generate the Eigenvalues and Eigenvectors I used the code below to generate the PCA Plot

setwd("/home/bambrozi/2_CORTISOL/PCA")

library(ggplot2) 
library(tidyverse)

##
# Visualize PCA results
###

# read in result files
eigenValues <- read_delim("pca_result.eigenval", delim = " ", col_names = F)
eigenVectors <- read_delim("pca_result.eigenvec", delim = " ", col_names = F)

## Proportion of variation captured by each vector
eigen_percent <- round((eigenValues / (sum(eigenValues))*100), 2)


# PCA plot
png("pca-plink.eng.png", width=5, height=5, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 1, alpha = 1) +
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()


# PCA plot with animal ids
png("pca-plink.eng.animals_id.png", width=50, height=50, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 5, alpha = 1) +
  geom_text(mapping = aes(x = X3, y = X4, label = X2), size = 2, hjust = 0, vjust = 0) +  # Add labels for animal IDs
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()

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8 GWAS on GCTA

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

This is te output from the code above:

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9 5%

9.1 Manhattan Plot - Bonferroni

gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)
gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}
bonf<- -log10(0.05/nrow(gwas))

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topleft", col="red2", lwd=2, c("Bonferroni correction"), bty="n")

The code above creates a Manhattam Plot, the correction for multiple test is the Bonferroni correction

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The code below will save information of Significant SNP.

library(tidyverse)

SNP_sig_bonf <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

Below we can see the 1 significant SNP for Bonferroni correction.

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9.2 FDR

gwas$bh <- p.adjust(gwas$p, method = "BH") #FDR

The code below will create a file with the significant snp for FDR-BH

SNP_sig_BH <- filter(gwas, bh < 0.05)
write.table(SNP_sig_BH, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_BH.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

As outcome we can see that the FDR-BH method didn’t increase the number of significant SNP.

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9.3 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)

gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf<- -log10(0.05/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topleft", col="red2", lwd=2, c("Bonferroni corr. for ind. segments"), bty="n")


library(tidyverse)

SNP_sig_bonf_ind_seg <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf_ind_seg, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

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Below we can find the list of significant SNPs after the correction for Multiple Testing

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10 qqPlot

I performed the qqPlot analysis using the code below:

gwas<- read.table("GWAS_result.loco.mlma", h=T)
ps<- gwas$p
inflation <- function(ps) {
  chisq <- qchisq(1 - ps, 1)
  lambda <- median(chisq) / qchisq(0.5, 1)
  lambda
}
# Calculating the lambda -  the lambda statistic should be close to 1 if
#the points fall within the expected range, or greater than one if 
# the observed p-values are more significant than expected.
inflation(ps)

bonf<- -log10(0.05/nrow(gwas))

gwas$log<- -log10(gwas$p)

gg_qqplot <- function(ps, ci = 0.95) {
  n  <- length(ps)
  df <- data.frame(
    observed = -log10(sort(ps)),
    expected = -log10(ppoints(n)),
    clower   = -log10(qbeta(p = (1 - ci) / 2, shape1 = 1:n, shape2 = n:1)),
    cupper   = -log10(qbeta(p = (1 + ci) / 2, shape1 = 1:n, shape2 = n:1))
  )
  log10Pe <- expression(paste("Expected -log"[10], plain(P)))
  log10Po <- expression(paste("Observed -log"[10], plain(P)))
  ggplot(df) +
    geom_ribbon(
      mapping = aes(x = expected, ymin = clower, ymax = cupper),
      alpha = 0.1
    ) +
    geom_point(aes(expected, observed), shape = 1, size = 3) +
    geom_abline(intercept = 0, slope = 1, alpha = 0.5) +
    # geom_line(aes(expected, cupper), linetype = 2, size = 0.5) +
    # geom_line(aes(expected, clower), linetype = 2, size = 0.5) +
    xlab(log10Pe) +
    ylab(log10Po)
}

## plot -----
gg_qqplot(ps) +
  theme_bw(base_size = 24) +
  annotate(
    geom = "text",
    x = -Inf,
    y = Inf,
    hjust = -0.15,
    vjust = 1 + 0.15 * 3,
    label = sprintf("λ = %.2f", inflation(ps)),
    size = 8
  ) +
  theme(
    axis.ticks = element_line(size = 0.5),
    panel.grid = element_blank()
    # panel.grid = element_line(size = 0.5, color = "grey80")
  )

The outcome is the qqplot below, and the lambda value is \(\lambda\) = 1.03

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11 VEP - Variant Effect Prediction

After insert all rsID on VEP the summary is shown below:

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Bellow I’m gonna show the “genome view” for each SNP (variant) recovered from VEP:

rs42217767 ps. this variant wasn’t recovered automaticaly by VEP, so I typed the chr and position on the search area of the “genome viewer” My Image

rs110031217 My Image

rs110991998 My Image

rs42089058 My Image

rs41644634 My Image

rs41567074 My Image

ps. the unique reference genome available in the VEP is the ARS-UCD1.3, which is not a problem once it is working with the rsID, which for sure is our variant.

12 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg.txt", 
                   head=T, stringsAsFactors = F)

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/out_genes_50k.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/out_genes_50k.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/out_qtl_50k.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/out_qtl_50k_clean.csv")

Dowloading the .gtf file from Ensembl https://useast.ensembl.org/info/data/ftp/index.html

Downloading the .gff file from AnimalQTLdb https://www.animalgenome.org/cgi-bin/QTLdb/index

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	11	BTB-01060598	32301594	11	32278324	32766620	488297	-	ENSBTAG00000046199	NRXN1	protein_coding
2	11	BTB-01060598	32301594	11	32280416	32280512	97	-	ENSBTAG00000044573	U6	snRNA
3	22	ARS-BFGL-NGS-26419	46052082	22	45924535	46818511	893977	-	ENSBTAG00000013117	CACNA2D3	protein_coding
4	22	ARS-BFGL-NGS-26419	46052082	22	46051998	46062657	10660	+	ENSBTAG00000013124	LRTM1	protein_coding
5	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding
6	26	BTB-00926636	17857480	26	17703169	17846407	143239	-	ENSBTAG00000011743	TLL2	protein_coding
7	26	BTB-00926636	17857480	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
8	26	BTA-62125-no-rs	17899619	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
9	26	BTA-62125-no-rs	17899619	26	17925643	18029878	104236	-	ENSBTAG00000019872	PIK3AP1	protein_coding
10	28	BTA-99379-no-rs	41666186	28	41609015	41649016	40002	-	ENSBTAG00000007540	GLUD1	protein_coding
11	28	BTA-99379-no-rs	41666186	28	41650333	41763371	113039	+	ENSBTAG00000019194	SHLD2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
2	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
3	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601
4	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51802
5	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51803
6	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51804
7	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51805
8	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51806
9	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51807
10	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51808
11	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51809
12	22	ARS-BFGL-NGS-26419	46052082	Health	46052080	46052084	51810
13	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51811
14	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51812
15	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51813
16	22	ARS-BFGL-NGS-26419	46052082	Milk	46073517	46073521	243365
17	26	BTB-00926636	17857480	Milk	17809768	17809772	195264
18	26	BTB-00926636	17857480	Milk	17809768	17809772	200269
19	26	BTB-00926636	17857480	Milk	17809768	17809772	200270
20	26	BTB-00926636	17857480	Milk	17809768	17809772	205295
21	26	BTB-00926636	17857480	Milk	17810363	17810367	198859
22	26	BTB-00926636	17857480	Milk	17810363	17810367	203868
23	26	BTB-00926636	17857480	Milk	17810363	17810367	203869
24	26	BTB-00926636	17857480	Milk	17810363	17810367	210297
25	26	BTB-00926636	17857480	Milk	17811021	17811025	198858
26	26	BTB-00926636	17857480	Milk	17811021	17811025	203866
27	26	BTB-00926636	17857480	Milk	17811021	17811025	203867
28	26	BTB-00926636	17857480	Milk	17811021	17811025	210296
29	26	BTB-00926636	17857480	Milk	17811725	17811729	197493
30	26	BTB-00926636	17857480	Milk	17811725	17811729	202704
31	26	BTB-00926636	17857480	Milk	17812273	17812277	195685
32	26	BTB-00926636	17857480	Milk	17812273	17812277	200785
33	26	BTB-00926636	17857480	Milk	17813212	17813216	195285
34	26	BTB-00926636	17857480	Milk	17813212	17813216	200301
35	26	BTB-00926636	17857480	Milk	17813978	17813982	196236
36	26	BTB-00926636	17857480	Milk	17813978	17813982	201468
37	26	BTB-00926636	17857480	Milk	17815237	17815241	198002
38	26	BTB-00926636	17857480	Milk	17815237	17815241	203190
39	26	BTB-00926636	17857480	Milk	17817223	17817227	34750
40	26	BTB-00926636	17857480	Milk	17817223	17817227	195586
41	26	BTB-00926636	17857480	Milk	17821297	17821301	197235
42	26	BTB-00926636	17857480	Milk	17821297	17821301	202455
43	26	BTB-00926636	17857480	Milk	17822209	17822213	197783
44	26	BTB-00926636	17857480	Milk	17822209	17822213	202983
45	26	BTB-00926636	17857480	Milk	17823105	17823109	196204
46	26	BTB-00926636	17857480	Milk	17823105	17823109	201419
47	26	BTB-00926636	17857480	Milk	17824770	17824774	196867
48	26	BTB-00926636	17857480	Milk	17824770	17824774	202101
49	26	BTB-00926636	17857480	Milk	17833551	17833555	195748
50	26	BTB-00926636	17857480	Milk	17833551	17833555	200855
51	26	BTB-00926636	17857480	Milk	17833551	17833555	206213
52	26	BTB-00926636	17857480	Milk	17865069	17865073	197144
53	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	197144
54	26	BTB-00926636	17857480	Milk	17865069	17865073	202367
55	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	202367
56	26	BTB-00926636	17857480	Milk	17865069	17865073	208485
57	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	208485
58	26	BTB-00926636	17857480	Milk	17871201	17871205	197611
59	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	197611
60	26	BTB-00926636	17857480	Milk	17871201	17871205	202816
61	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	202816
62	26	BTB-00926636	17857480	Milk	17871201	17871205	209076
63	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	209076
64	26	BTB-00926636	17857480	Milk	17885871	17885875	63656
65	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	63656
66	26	BTB-00926636	17857480	Milk	17885871	17885875	199068
67	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	199068
68	26	BTB-00926636	17857480	Milk	17885871	17885875	204113
69	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204113
70	26	BTB-00926636	17857480	Milk	17885871	17885875	204114
71	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204114
72	26	BTB-00926636	17857480	Milk	17885871	17885875	210399
73	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	210399
74	26	BTB-00926636	17857480	Milk	17895273	17895277	63657
75	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	63657
76	26	BTB-00926636	17857480	Milk	17895273	17895277	199069
77	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	199069
78	26	BTB-00926636	17857480	Milk	17895273	17895277	204115
79	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204115
80	26	BTB-00926636	17857480	Milk	17895273	17895277	204116
81	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204116
82	26	BTB-00926636	17857480	Milk	17895273	17895277	210400
83	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	210400
84	26	BTB-00926636	17857480	Milk	17895803	17895807	199070
85	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	199070
86	26	BTB-00926636	17857480	Milk	17895803	17895807	204117
87	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204117
88	26	BTB-00926636	17857480	Milk	17895803	17895807	204118
89	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204118
90	26	BTB-00926636	17857480	Milk	17895803	17895807	210401
91	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	210401
92	26	BTB-00926636	17857480	Milk	17900304	17900308	199071
93	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	199071
94	26	BTB-00926636	17857480	Milk	17900304	17900308	204119
95	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204119
96	26	BTB-00926636	17857480	Milk	17900304	17900308	204120
97	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204120
98	26	BTB-00926636	17857480	Milk	17900304	17900308	210402
99	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	210402
100	26	BTB-00926636	17857480	Milk	17902932	17902936	63658
101	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	63658
102	26	BTB-00926636	17857480	Milk	17902932	17902936	199072
103	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	199072
104	26	BTB-00926636	17857480	Milk	17902932	17902936	204121
105	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204121
106	26	BTB-00926636	17857480	Milk	17902932	17902936	204122
107	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204122
108	26	BTB-00926636	17857480	Milk	17902932	17902936	210403
109	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	210403
110	26	BTB-00926636	17857480	Milk	17906861	17906865	63660
111	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	63660
112	26	BTB-00926636	17857480	Milk	17906861	17906865	199074
113	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	199074
114	26	BTB-00926636	17857480	Milk	17906861	17906865	204125
115	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204125
116	26	BTB-00926636	17857480	Milk	17906861	17906865	204126
117	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204126
118	26	BTB-00926636	17857480	Milk	17906861	17906865	210405
119	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	210405
120	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	63654
121	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	199076
122	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204129
123	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204130
124	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	210407
125	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	63655
126	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	197826
127	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203026
128	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203027
129	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	209354
130	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	195860
131	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201008
132	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201009
133	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	206462
134	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	33984
135	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195668
136	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195669
137	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195670
138	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	200758
139	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	199092
140	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	204151
141	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107048
142	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107227

12.1 QTL annotation on GALLO

QTL type

#GALLO
par(mar=c(8,20,8,8))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1)

My Image

QTL type

#GALLO
par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

Reproduction My Image

Health My Image

12.2 QTL enrichment on GALLO

#GALLO
#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/out_enrich_qtl_genome_name.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/out_enrich_qtl_genome_type.csv")

#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
5	Milk C14 index	35	4437	108	163224	0.0000000	0.0000000	Milk
9	Milk myristoleic acid content	26	3047	108	163224	0.0000000	0.0000000	Milk
10	Milk palmitoleic acid content	15	2422	108	163224	0.0000000	0.0000000	Milk
6	Milk C16 index	12	2002	108	163224	0.0000000	0.0000001	Milk
12	Muscle carnosine content	3	67	108	163224	0.0000131	0.0000497	Meat and Carcass
14	Pregnancy rate	2	944	108	163224	0.1296627	0.4105985	Reproduction
18	Teat length	1	300	108	163224	0.1802436	0.4892327	Exterior
15	Rear leg placement - side view	1	430	108	163224	0.2479752	0.5235032	Exterior
17	Stillbirth	2	1363	108	163224	0.2280294	0.5235032	Reproduction
3	Foot angle	1	672	108	163224	0.3596272	0.6162877	Exterior
7	Milk capric acid content	1	912	108	163224	0.4541067	0.6162877	Milk
8	Milk myristic acid content	1	902	108	163224	0.4504612	0.6162877	Milk
13	Net merit	1	903	108	163224	0.4508269	0.6162877	Production
19	Udder depth	1	695	108	163224	0.3693423	0.6162877	Exterior
16	Somatic cell score	1	1122	108	163224	0.5253603	0.6654564	Health
2	Conception rate	1	1255	108	163224	0.5656375	0.6716945	Reproduction
1	Calving ease	2	3819	108	163224	0.7219243	0.7776744	Reproduction
4	Length of productive life	1	2004	108	163224	0.7367441	0.7776744	Production
11	Milk yield	1	6432	108	163224	0.9870080	0.9870080	Milk

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	4	9077	108	163224	0.8569775	0.9999953
2	Health	1	5889	108	163224	0.9811250	0.9999953
3	Meat and Carcass	3	18258	108	163224	0.9997109	0.9999953
4	Milk	91	75352	108	163224	0.0000000	0.0000000
5	Production	2	19640	108	163224	0.9999848	0.9999953
6	Reproduction	7	35008	108	163224	0.9999953	0.9999953

My Image

13 GPROFILER

online version: https://biit.cs.ut.ee/gprofiler/gost

I got a script from Julia Rodrigues about the R package GPROFILER to perform an enrichment of my Genes.

### enriquecimento genico
#install.packages("gprofiler2")
library(gprofiler2)

#Para conferir a lista de organism -> https://biit.cs.ut.ee/gprofiler/page/organism-list

#Obs: eu entro com os ids ENSOAR...
query <- read.table ("/home/bambrozi/2_CORTISOL/GALLO/out_genes_50k.txt", header = T)
query <- query[,c("gene_id")]

gene_enrich <- gost(
  query,
  organism = "btaurus", 
  ordered_query = FALSE,
  multi_query = FALSE,
  significant = TRUE,
  exclude_iea = FALSE,
  measure_underrepresentation = FALSE,
  evcodes = TRUE,
  user_threshold = 0.05,
  correction_method = c("fdr"),
  domain_scope = c("annotated"),
  numeric_ns = "",
  sources = NULL,
  as_short_link = FALSE,
  highlight = FALSE
)


#str(gene_enrich) # para ver o formato dos meus dados

#selecionando apenas as informações da lista que me interessam para fazer meu data.frame 
result_enrich <- data.frame(gene_enrich$result)
result_enrich <- data.frame(Category = result_enrich$source,
                            ID = result_enrich$term_id,
                            Term = result_enrich$term_name,
                            adj_pvalue = result_enrich$p_value,
                            id_ensembl = result_enrich$intersection)

write.table(result_enrich,"/home/bambrozi/2_CORTISOL/GPROFILER/gene_enrich.txt", col.names=TRUE, row.names=FALSE, sep="\t", quote=F)
write.csv(result_enrich,"/home/bambrozi/2_CORTISOL/GPROFILER/gene_enrich.csv")

Below we can se the significant terms of this enrichment (output):

X	Category	ID	Term	adj_pvalue	id_ensembl
1	GO:BP	GO:0006538	glutamate catabolic process	0.0383792	ENSBTAG00000007540
2	GO:BP	GO:0003274	endocardial cushion fusion	0.0383792	ENSBTAG00000019832
3	GO:BP	GO:1905005	regulation of epithelial to mesenchymal transition involved in endocardial cushion formation	0.0383792	ENSBTAG00000019832
4	GO:BP	GO:1905007	positive regulation of epithelial to mesenchymal transition involved in endocardial cushion formation	0.0383792	ENSBTAG00000019832
5	GO:BP	GO:0003430	growth plate cartilage chondrocyte growth	0.0383792	ENSBTAG00000019832
6	GO:BP	GO:0003431	growth plate cartilage chondrocyte development	0.0383792	ENSBTAG00000019832
7	GO:BP	GO:0003433	chondrocyte development involved in endochondral bone morphogenesis	0.0383792	ENSBTAG00000019832
8	GO:BP	GO:0003415	chondrocyte hypertrophy	0.0383792	ENSBTAG00000019832
9	GO:BP	GO:0048631	regulation of skeletal muscle tissue growth	0.0383792	ENSBTAG00000011743
10	GO:BP	GO:0051138	positive regulation of NK T cell differentiation	0.0383792	ENSBTAG00000019832
11	GO:BP	GO:0062043	positive regulation of cardiac epithelial to mesenchymal transition	0.0383792	ENSBTAG00000019832
12	GO:BP	GO:0051136	regulation of NK T cell differentiation	0.0383792	ENSBTAG00000019832
13	GO:BP	GO:0003186	tricuspid valve morphogenesis	0.0383792	ENSBTAG00000019832
14	GO:BP	GO:0062042	regulation of cardiac epithelial to mesenchymal transition	0.0383792	ENSBTAG00000019832
15	GO:BP	GO:0060434	bronchus morphogenesis	0.0383792	ENSBTAG00000019832
16	GO:BP	GO:0060440	trachea formation	0.0383792	ENSBTAG00000019832
17	GO:BP	GO:0060462	lung lobe development	0.0383792	ENSBTAG00000019832
18	GO:BP	GO:0060463	lung lobe morphogenesis	0.0383792	ENSBTAG00000019832
19	GO:BP	GO:0048632	negative regulation of skeletal muscle tissue growth	0.0383792	ENSBTAG00000011743
20	GO:BP	GO:0003175	tricuspid valve development	0.0383792	ENSBTAG00000019832
21	GO:BP	GO:0061520	Langerhans cell differentiation	0.0383792	ENSBTAG00000019832
22	GO:BP	GO:1905317	inferior endocardial cushion morphogenesis	0.0383792	ENSBTAG00000019832
23	GO:BP	GO:2000563	positive regulation of CD4-positive, alpha-beta T cell proliferation	0.0383792	ENSBTAG00000019832
24	GO:BP	GO:1990428	miRNA transport	0.0383792	ENSBTAG00000019832
25	GO:BP	GO:0002513	tolerance induction to self antigen	0.0383792	ENSBTAG00000019832
26	GO:BP	GO:0002514	B cell tolerance induction	0.0383792	ENSBTAG00000019832
27	GO:BP	GO:0003149	membranous septum morphogenesis	0.0383792	ENSBTAG00000019832
28	GO:BP	GO:1990086	lens fiber cell apoptotic process	0.0383792	ENSBTAG00000019832
29	GO:BP	GO:0002520	immune system development	0.0383792	ENSBTAG00000019832,ENSBTAG00000019194
30	GO:BP	GO:0002666	positive regulation of T cell tolerance induction	0.0383792	ENSBTAG00000019832
31	GO:BP	GO:0002651	positive regulation of tolerance induction to self antigen	0.0383792	ENSBTAG00000019832
32	GO:BP	GO:0061343	cell adhesion involved in heart morphogenesis	0.0383792	ENSBTAG00000019832
33	GO:BP	GO:0002649	regulation of tolerance induction to self antigen	0.0383792	ENSBTAG00000019832
34	GO:BP	GO:0002661	regulation of B cell tolerance induction	0.0383792	ENSBTAG00000019832
35	GO:BP	GO:0002664	regulation of T cell tolerance induction	0.0383792	ENSBTAG00000019832
36	GO:BP	GO:0002663	positive regulation of B cell tolerance induction	0.0383792	ENSBTAG00000019832
37	GO:BP	GO:0002684	positive regulation of immune system process	0.0428818	ENSBTAG00000019832,ENSBTAG00000019872,ENSBTAG00000019194
38	GO:BP	GO:0048630	skeletal muscle tissue growth	0.0431888	ENSBTAG00000011743
39	GO:BP	GO:0051251	positive regulation of lymphocyte activation	0.0431888	ENSBTAG00000019832,ENSBTAG00000019194
40	GO:BP	GO:0034154	toll-like receptor 7 signaling pathway	0.0431888	ENSBTAG00000019872
41	GO:BP	GO:0060439	trachea morphogenesis	0.0431888	ENSBTAG00000019832
42	GO:BP	GO:0002517	T cell tolerance induction	0.0431888	ENSBTAG00000019832
43	GO:BP	GO:0002645	positive regulation of tolerance induction	0.0431888	ENSBTAG00000019832
44	GO:BP	GO:0060433	bronchus development	0.0460331	ENSBTAG00000019832
45	GO:BP	GO:0003418	growth plate cartilage chondrocyte differentiation	0.0460331	ENSBTAG00000019832
46	GO:BP	GO:0002696	positive regulation of leukocyte activation	0.0482941	ENSBTAG00000019832,ENSBTAG00000019194
47	GO:BP	GO:0001865	NK T cell differentiation	0.0489635	ENSBTAG00000019832
48	GO:BP	GO:0050867	positive regulation of cell activation	0.0496277	ENSBTAG00000019832,ENSBTAG00000019194
49	GO:BP	GO:0003198	epithelial to mesenchymal transition involved in endocardial cushion formation	0.0496277	ENSBTAG00000019832
50	GO:BP	GO:0002643	regulation of tolerance induction	0.0496277	ENSBTAG00000019832
51	GO:BP	GO:2001034	positive regulation of double-strand break repair via nonhomologous end joining	0.0496277	ENSBTAG00000019194
52	GO:MF	GO:0004352	glutamate dehydrogenase (NAD+) activity	0.0098128	ENSBTAG00000007540
53	GO:MF	GO:0004353	glutamate dehydrogenase [NAD(P)+] activity	0.0098128	ENSBTAG00000007540
54	GO:MF	GO:0016639	oxidoreductase activity, acting on the CH-NH2 group of donors, NAD or NADP as acceptor	0.0098128	ENSBTAG00000007540
55	GO:MF	GO:0005026	transforming growth factor beta receptor activity, type II	0.0147171	ENSBTAG00000019832
56	GO:MF	GO:0048185	activin binding	0.0326677	ENSBTAG00000019832
57	GO:MF	GO:0036312	phosphatidylinositol 3-kinase regulatory subunit binding	0.0326677	ENSBTAG00000019872
58	GO:MF	GO:0034713	type I transforming growth factor beta receptor binding	0.0326677	ENSBTAG00000019832
59	GO:MF	GO:0017002	activin receptor activity	0.0326677	ENSBTAG00000019832
60	GO:MF	GO:0005024	transforming growth factor beta receptor activity	0.0326677	ENSBTAG00000019832
61	GO:MF	GO:0050431	transforming growth factor beta binding	0.0461234	ENSBTAG00000019832
62	GO:MF	GO:0043548	phosphatidylinositol 3-kinase binding	0.0461234	ENSBTAG00000019872
63	GO:MF	GO:0016638	oxidoreductase activity, acting on the CH-NH2 group of donors	0.0461234	ENSBTAG00000007540
64	GO:MF	GO:0005160	transforming growth factor beta receptor binding	0.0461234	ENSBTAG00000019832
65	GO:MF	GO:0004675	transmembrane receptor protein serine/threonine kinase activity	0.0461234	ENSBTAG00000019832
66	REAC	REAC:R-BTA-2243919	Crosslinking of collagen fibrils	0.0440084	ENSBTAG00000011743
67	REAC	REAC:R-BTA-2173788	Downregulation of TGF-beta receptor signaling	0.0440084	ENSBTAG00000019832
68	REAC	REAC:R-BTA-8964539	Glutamate and glutamine metabolism	0.0440084	ENSBTAG00000007540
69	REAC	REAC:R-BTA-112308	Presynaptic depolarization and calcium channel opening	0.0440084	ENSBTAG00000013117
70	REAC	REAC:R-BTA-2151201	Transcriptional activation of mitochondrial biogenesis	0.0440084	ENSBTAG00000007540
71	REAC	REAC:R-BTA-2173791	TGF-beta receptor signaling in EMT (epithelial to mesenchymal transition)	0.0440084	ENSBTAG00000019832

13.1 GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend My Image

My Image

Additionaly I perfomed a another version of this analysis but with no eletronic GO annotation

My Image

13.2 Terms distribution Pie Chart

Now I’m gonna make a pie chart with the categories

library(ggplot2)

# Assuming sig_enrich$Category contains the categories
category_counts <- table(result_enrich$Category)

# Create a pie chart
pie_chart <- ggplot(data = NULL, aes(x = factor(1), fill = names(category_counts), y = as.numeric(category_counts))) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar(theta = "y") +
  labs(title = "Distribution among terms",
       x = NULL,
       y = NULL,
       fill = "Category") +
  theme_void() +
  theme(legend.position = "right")

# Print the pie chart
print(pie_chart)

My Image

14 Haplotype Blocks

First I need to generate the .ped file

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file

plink \
    --bfile ${DATA} \
    --cow \
    --recode \
    --out ${RESULT}

Second, is necessary to convert the .ped file to Linkage format:

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/hapblock_in

plink \
    --file ${DATA} \
    --cow \
    --recode HV \
    --out ${RESULT}

The code above will generate a pair of files .ped and .info for each chromosome

I ran the haploview only for chromosome 11, 22, 26, 28 that are when the significant SNP are located.

As the LD Plot bring the SNP ordered, first I looked at /home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg.txt, to know the SNP position, and then compare with the Check Markers tab in the haploview SNP position x SNP# in plot

My Image

Chr	SNP	bp	A1	A2	Freq	Haploview.order
11	BTB-01060598	32301594	C	A	0.4503970	680
22	ARS-BFGL-NGS-23411	5134078	A	C	0.1865080	88
22	ARS-BFGL-NGS-26419	46052082	A	C	0.0813492	762
26	BTB-00926636	17857480	C	A	0.0257937	296
26	BTA-62125-no-rs	17899619	C	A	0.0257937	297
28	BTA-99379-no-rs	41666186	A	C	0.0496032	701

The two significant SNPs from chromosome 26 fell within a Haploblock.

Chr 11 (SNP 680) My Image

Chr 22 (SNP 88) My Image

Chr 22 (SNP 762) My Image

Chr 26 (SNP 296 and 297) My Image

Chr 28 (SNP 701) My Image

14.1 BLOCK 10 in Chr 26

As we found an haploblock on chromosome 26 I decided take a look inside this block. So, on Haploview I checked the name and position of the first SNP of this block (292) and the last (300).

X.	Name	Position	ObsHET	PredHET	HWpval	X.Geno	MAF	Alleles
292	ARS-BFGL-NGS-111577	17714604	0.401	0.448	0.1161	100	0.339	A:C
293	ARS-BFGL-NGS-21408	17746468	0.369	0.403	0.2260	100	0.280	C:A
294	ARS-BFGL-NGS-16328	17778908	0.056	0.054	1.0000	100	0.028	A:C
295	ARS-BFGL-NGS-117063	17817225	0.413	0.452	0.2022	100	0.345	C:A
296	BTB-00926636	17857480	0.052	0.050	1.0000	100	0.026	A:C
297	BTA-62125-no-rs	17899619	0.052	0.050	1.0000	100	0.026	A:C
298	ARS-BFGL-NGS-110679	17953795	0.397	0.433	0.2211	100	0.317	A:C
299	ARS-BFGL-NGS-72889	17989430	0.052	0.050	1.0000	100	0.026	C:A
300	BTB-00926868	18040673	0.385	0.420	0.2319	100	0.300	A:C

Then I checked on NCBI’s Genome Data Viewer which genes are inside this block, adding 50k before the first SNP and 50K after the last SNP.

This interval has 426,069bp and has this genes inside:

DNTT
OPALIN
TLL2
TM9SF3
PIK3AP1

As you can see in the image below:

My Image

The genes TLL2, TM9SF3 and PIK3AP1 already were in the gene list from GALLO, but the genes DNTT and OPALIN were new!

I performed a GPROFILER analysis only for these genes spotted in this Block 10.

My Image

15 5 - 15%

After check the outcome for 5% of significance for SNPs, Prof. Flávio asked me to carry out again considering the interval between 5% and 15% of significance.

15.1 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)

gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf05<- -log10(0.05/Me)
bonf15<- -log10(0.15/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf05), col="black")
abline(h=(bonf15), col="red2")
legend("topleft", inset=c(0.05, 0.05), col="red2", lwd=2, 
       legend=c("15% sig. Bonferroni corr. for ind. segments"), bty="n")
legend("topleft", inset=c(0.05, 0.10), col="black", lwd=2, 
       legend=c("5% sig. Bonferroni corr. for ind. segments"), bty="n")

library(tidyverse)

#Recovering the significant SNPs > <5%
SNP_sig_bonf_ind_seg_5 <- filter(gwas, logP > bonf05)
write.csv(SNP_sig_bonf_ind_seg_5, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_5.csv")


#Recovering the significant SNPs > 15% and <5%
SNP_sig_bonf_ind_seg_5_15 <- filter(gwas, logP > bonf15 & logP < bonf05)
write.csv(SNP_sig_bonf_ind_seg_5_15, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_5_15.csv")


#Recovering the significant SNPs > 15% 
SNP_sig_bonf_ind_seg_15 <- filter(gwas, logP > bonf15)
write.csv(SNP_sig_bonf_ind_seg_15, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_15.csv")

My Image

Below we can find the list of significant SNPs after the correction for Multiple Testing

Chr	X	X15.	X5.	X5.15.	logP
28	BTA-99379-no-rs	X	X		6.610970
22	ARS-BFGL-NGS-23411	X	X		5.485541
22	ARS-BFGL-NGS-26419	X	X		5.272074
26	BTA-62125-no-rs	X	X		4.732155
26	BTB-00926636	X	X		4.732155
11	BTB-01060598	X	X		4.682532
20	BTB-01471673	X		X	4.182974
23	Hapmap54016-rs29022819	X		X	4.016453
28	ARS-BFGL-NGS-11935	X		X	3.986177
28	Hapmap43005-BTA-64606	X		X	3.986177
11	BTA-122098-no-rs	X		X	3.980028
19	ARS-BFGL-NGS-29119	X		X	3.925461
14	Hapmap39823-BTA-35254	X		X	3.905277
19	ARS-BFGL-NGS-28901	X		X	3.888442
3	ARS-BFGL-NGS-4585	X		X	3.885943
14	UA-IFASA-7664	X		X	3.882503

To make easier to check the name of the significant SNPs I made the following Manhattan Plot

# Prepare the dataset
don <- gwas %>% 
  group_by(Chr) %>% 
  summarise(chr_len = max(bp)) %>% 
  mutate(tot = cumsum(as.numeric(chr_len)) - as.numeric(chr_len)) %>%
  select(-chr_len) %>%
  left_join(gwas, ., by = c("Chr" = "Chr")) %>%
  arrange(Chr, bp) %>%
  mutate(BPcum = as.numeric(bp) + tot) %>%
  mutate(is_highlight = ifelse(logP > bonf15, "yes", "no")) %>%
  mutate(is_annotate = ifelse(logP > bonf15, "yes", "no"))

# Remove rows with missing BPcum values
don <- don %>% filter(!is.na(BPcum))

# Prepare X axis
axisdf <- don %>% 
  group_by(Chr) %>% 
  summarize(center = (max(BPcum) + min(BPcum)) / 2)

# Find the maximum logP value for setting y-axis limits
max_logP <- max(don$logP, na.rm = TRUE)

# Make the plot
ggplot(don, aes(x = BPcum, y = logP)) +
  geom_point(aes(color = as.factor(Chr)), alpha = 0.8, size = 1.3) +
  scale_color_manual(values = rep(c("grey", "skyblue"), 22)) +
  scale_x_continuous(label = axisdf$Chr, breaks = axisdf$center) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, max_logP + 1)) + # Set y-axis limit
  geom_hline(yintercept = bonf05, color = "black") +  # Add horizontal line for bonf05
  geom_hline(yintercept = bonf15, color = "red2") +   # Add horizontal line for bonf15
  geom_point(data = subset(don, is_highlight == "yes"), color = "orange", size = 2) +
  geom_text_repel(data = subset(don, is_annotate == "yes"), aes(label = SNP), size = 2) +
  annotate("text", x = Inf, y = Inf, label = "15% sig. Bonferroni corr. for ind. segments", 
           color = "red2", hjust = 1.1, vjust = 2, size = 3) +
  annotate("text", x = Inf, y = Inf, label = "5% sig. Bonferroni corr. for ind. segments", 
           color = "black", hjust = 1.1, vjust = 3.5, size = 3) +
  theme_bw() +
  theme(
    legend.position = "none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  labs(title = "GWAS Cortisol", x = "Chromosomes", y = "-log10(p)")

My Image

Generating a file with the GWAS output and rsID

library(tidyverse)

# Extracting SNP names to use in the SNPChimp
SNP_NAMES <- SNP_sig_bonf_ind_seg_15 %>% pull(SNP)
SNP_NAMES <- paste(SNP_NAMES, collapse = ", ")
print(SNP_NAMES)

# Copy this printed names and paste on SNPChimp

# Opening the SNPChimp output in R
rsID <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/SNPchimp_result_ind_seg_15.tsv")
rsID <- rsID[,c("rs","SNP_name")]

#Merging in one file the GWAS output and rsID
SNP_rsID_sig_bonf_ind_seg_15 <- merge(SNP_sig_bonf_ind_seg_15, rsID, by.x = "SNP", by.y = "SNP_name")
SNP_rsID_sig_bonf_ind_seg_15 <- SNP_rsID_sig_bonf_ind_seg_15[, c("rs", "SNP", "Chr", "bp", "A1", "A2", "Freq", "b", "se", "p", "logP")]
write.csv(SNP_rsID_sig_bonf_ind_seg_15, "/home/bambrozi/2_CORTISOL/GWAS/SNP_rsID_bonf_ind_seg_15.csv")

Bellow we can see the table with the SNP names and rsID for all significant SNPs with p-value lower than 0,15.

X	rs	SNP	Chr	bp	A1	A2	Freq	b	se	p	logP
1	rs109578343	ARS-BFGL-NGS-11935	28	44942879	A	C	0.0297619	412.261	106.1750	0.0001032	3.986177
2	rs110031217	ARS-BFGL-NGS-23411	22	5134078	A	C	0.1865080	207.116	44.5110	0.0000033	5.485541
3	rs110991998	ARS-BFGL-NGS-26419	22	46052082	A	C	0.0813492	308.357	67.7592	0.0000053	5.272074
4	rs109751680	ARS-BFGL-NGS-28901	19	42338148	C	A	0.3710320	141.156	36.8765	0.0001293	3.888442
5	rs41577586	ARS-BFGL-NGS-29119	19	49744592	A	C	0.4047620	-142.084	36.9169	0.0001187	3.925461
6	rs109888380	ARS-BFGL-NGS-4585	3	107160292	A	C	0.3353170	-144.488	37.7607	0.0001300	3.885943
7	rs41624254	BTA-122098-no-rs	11	31882807	A	C	0.4444440	131.779	33.9688	0.0001047	3.980028
8	rs41644634	BTA-62125-no-rs	26	17899619	C	A	0.0257937	487.149	113.7690	0.0000185	4.732155
9	rs41567074	BTA-99379-no-rs	28	41666186	A	C	0.0496032	435.809	84.4338	0.0000002	6.610970
10	rs42089058	BTB-00926636	26	17857480	C	A	0.0257937	487.149	113.7690	0.0000185	4.732155
11	rs42217767	BTB-01060598	11	32301594	C	A	0.4503970	-152.746	35.8860	0.0000208	4.682532
12	rs42590312	BTB-01471673	20	11545685	A	C	0.2460320	169.707	42.5157	0.0000656	4.182974
13	rs41632193	Hapmap39823-BTA-35254	14	64061208	A	C	0.2916670	-138.029	35.9698	0.0001244	3.905277
14	rs41648479	Hapmap43005-BTA-64606	28	45805105	A	C	0.0297619	412.261	106.1750	0.0001032	3.986177
15	rs29022819	Hapmap54016-rs29022819	23	33609998	C	A	0.1904760	173.228	44.4201	0.0000963	4.016453
16	rs41632223	UA-IFASA-7664	14	64299714	C	A	0.2678570	-141.443	36.9840	0.0001311	3.882503

16 qqPlot

17 GWAS on GCTA - considering BIRTH_YEAR and SAMPLING_DATE

pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv", header = T)
ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)


# Create data_final$cdn_id by matching IDs with elora_id
data_final$cdn_id <- ids_eq$cdn_id[match(data_final$ID, ids_eq$elora_id)]
fixeff <- data_final[,c("ID", "BIRTH_YEAR", "Sampling_date", "cdn_id")]
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id, ]
fixeff$FID <- "HO"
fixeff <- fixeff[, c("FID", "cdn_id", "BIRTH_YEAR", "Sampling_date")]

identical(fixeff$cdn_id, pheno$cdn_id)

write.table(fixeff, "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/fixeff.txt", quote = F, row.names = F, col.names = T)

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

17.1 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)

gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf<- -log10(0.05/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topleft", col="red2", lwd=2, c("Bonferroni corr. for ind. segments"), bty="n")


library(tidyverse)

SNP_sig_bonf_ind_seg <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf_ind_seg, file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/SNP_sig_Bonf_ind_seg.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(SNP_sig_bonf_ind_seg, "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/SNP_sig_Bonf_ind_seg2.csv")

The SNPs below are the significant after the correction above

X	Chr	SNP	bp	A1	A2	Freq	b	se	p	logP
1	22	ARS-BFGL-NGS-23411	5134078	A	C	0.1872510	196.185	43.9934	8.20e-06	5.085250
2	22	ARS-BFGL-NGS-26419	46052082	A	C	0.0816733	295.245	66.9504	1.03e-05	4.985404
3	26	BTB-00926636	17857480	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
4	26	BTA-62125-no-rs	17899619	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
5	28	BTA-99379-no-rs	41666186	A	C	0.0498008	394.376	83.4532	2.30e-06	5.639587

My Image

Bellow we inserted the rsID searched at SNPCHIMP

X	Chr	rsID	SNP	bp	A1	A2	Freq	b	se	p	logP
1	22	rs110031217	ARS-BFGL-NGS-23411	5134078	A	C	0.1872510	196.185	43.9934	8.20e-06	5.085250
2	22	rs110991998	ARS-BFGL-NGS-26419	46052082	A	C	0.0816733	295.245	66.9504	1.03e-05	4.985404
3	26	rs42089058	BTB-00926636	17857480	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
4	26	rs41644634	BTA-62125-no-rs	17899619	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
5	28	rs41567074	BTA-99379-no-rs	41666186	A	C	0.0498008	394.376	83.4532	2.30e-06	5.639587

17.1.1 Option 2: Manhattan Plot

gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)
# Prepare the dataset
don <- gwas %>% 
  group_by(Chr) %>% 
  summarise(chr_len = max(bp)) %>% 
  mutate(tot = cumsum(as.numeric(chr_len)) - as.numeric(chr_len)) %>%
  select(-chr_len) %>%
  left_join(gwas, ., by = c("Chr" = "Chr")) %>%
  arrange(Chr, bp) %>%
  mutate(BPcum = as.numeric(bp) + tot) %>%
  mutate(is_highlight = ifelse(logP > bonf, "yes", "no")) %>%
  mutate(is_annotate = ifelse(logP > bonf, "yes", "no"))

# Remove rows with missing BPcum values
don <- don %>% filter(!is.na(BPcum))

# Prepare X axis
axisdf <- don %>% 
  group_by(Chr) %>% 
  summarize(center = (max(BPcum) + min(BPcum)) / 2)

# Find the maximum logP value for setting y-axis limits
max_logP <- max(don$logP, na.rm = TRUE)

# Make the plot
ggplot(don, aes(x = BPcum, y = logP)) +
  geom_point(aes(color = as.factor(Chr)), alpha = 0.8, size = 1.3) +
  scale_color_manual(values = rep(c("grey", "skyblue"), 22)) +
  scale_x_continuous(label = axisdf$Chr, breaks = axisdf$center) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, max_logP + 1)) + # Set y-axis limit
  geom_point(data = subset(don, is_highlight == "yes"), color = "orange", size = 2) +
  geom_text_repel(data = subset(don, is_annotate == "yes"), aes(label = SNP), size = 2) +
  theme_bw() +
  theme(
    legend.position = "none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  labs(title = "GWAS Cortisol", x = "Chromosomes", y = "-log10(p)")

18 VEP - Variant Effect Prediction

After insert all rsID on VEP the summary is shown below:

My Image

As can be seen, the VEP only analysed 14 variants

Bellow I’m gonna show the “genome view” for each SNP (variant) recovered from VEP:

rs41648479 My Image

rs41632223 My Image

rs41632193 My Image

rs42590312 My Image

rs42089058 My Image

rs41567074 My Image

rs41644634 My Image

rs41624254 My Image

rs109888380 My Image

rs41577586 My Image

rs109751680 My Image

rs110991998 My Image

rs110031217

rs109578343 My Image

ps. the unique reference genome available in the VEP is the ARS-UCD1.3, which is not a problem once it is working with the rsID, which for sure is our variant.

19 VEP - With Birth_year and Sampling_date

After insert all rsID on VEP the summary is shown below:

As can be seen, the VEP only analysed 14 variants

Bellow I’m gonna show the “genome view” for each SNP (variant) recovered from VEP:

rs110031217 My Image

rs110991998 My Image

rs42089058 My Image

rs41644634 My Image

rs41567074 My Image

ps. the unique reference genome available in the VEP is the ARS-UCD1.3, which is not a problem once it is working with the rsID, which for sure is our variant.

20 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.csv(file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_15.csv")

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_genes_50k_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_genes_50k_15.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_qtl_50k_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_qtl_50k_clean_15.csv")

Dowloading the .gtf file from Ensembl https://useast.ensembl.org/info/data/ftp/index.html

Downloading the .gff file from AnimalQTLdb https://www.animalgenome.org/cgi-bin/QTLdb/index

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	3	ARS-BFGL-NGS-4585	107160292	3	107109148	107110689	1542	+	ENSBTAG00000030338	GJA9	protein_coding
2	3	ARS-BFGL-NGS-4585	107160292	3	107111822	107117831	6010	+	ENSBTAG00000030337	MYCBP	protein_coding
3	3	ARS-BFGL-NGS-4585	107160292	3	107121920	107136338	14419	+	ENSBTAG00000009368	RRAGC	protein_coding
4	11	BTB-01060598	32301594	11	32278324	32766620	488297	-	ENSBTAG00000046199	NRXN1	protein_coding
5	11	BTB-01060598	32301594	11	32280416	32280512	97	-	ENSBTAG00000044573	U6	snRNA
6	14	Hapmap39823-BTA-35254	64061208	14	64047857	64103385	55529	+	ENSBTAG00000017833	RNF19A	protein_coding
7	14	UA-IFASA-7664	64299714	14	64289853	64398324	108472	+	ENSBTAG00000019793	RGS22	protein_coding
8	19	ARS-BFGL-NGS-29119	49744592	19	49662140	49766195	104056	+	ENSBTAG00000052072	B3GNTL1	protein_coding
9	19	ARS-BFGL-NGS-29119	49744592	19	49768681	49927209	158529	-	ENSBTAG00000015414	TBCD	protein_coding
10	19	ARS-BFGL-NGS-28901	42338148	19	42283857	42299780	15924	-	ENSBTAG00000001195	KCNH4	protein_coding
11	19	ARS-BFGL-NGS-28901	42338148	19	42302320	42303571	1252	-	ENSBTAG00000000665	HCRT	protein_coding
12	19	ARS-BFGL-NGS-28901	42338148	19	42307188	42312293	5106	-	ENSBTAG00000000666	GHDC	protein_coding
13	19	ARS-BFGL-NGS-28901	42338148	19	42319170	42357910	38741	-	ENSBTAG00000010125	STAT5B	protein_coding
14	19	ARS-BFGL-NGS-28901	42338148	19	42387164	42390885	3722	+	ENSBTAG00000052763	NA	lncRNA
15	22	ARS-BFGL-NGS-26419	46052082	22	45924535	46818511	893977	-	ENSBTAG00000013117	CACNA2D3	protein_coding
16	22	ARS-BFGL-NGS-26419	46052082	22	46051998	46062657	10660	+	ENSBTAG00000013124	LRTM1	protein_coding
17	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding
18	23	Hapmap54016-rs29022819	33609998	23	33618285	33618377	93	+	ENSBTAG00000045279	SNORA70	snoRNA
19	26	BTB-00926636	17857480	26	17703169	17846407	143239	-	ENSBTAG00000011743	TLL2	protein_coding
20	26	BTB-00926636	17857480	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
21	26	BTA-62125-no-rs	17899619	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
22	26	BTA-62125-no-rs	17899619	26	17925643	18029878	104236	-	ENSBTAG00000019872	PIK3AP1	protein_coding
23	28	BTA-99379-no-rs	41666186	28	41609015	41649016	40002	-	ENSBTAG00000007540	GLUD1	protein_coding
24	28	BTA-99379-no-rs	41666186	28	41650333	41763371	113039	+	ENSBTAG00000019194	SHLD2	protein_coding
25	28	Hapmap43005-BTA-64606	45805105	28	45757443	45768564	11122	+	ENSBTAG00000012393	AGT	protein_coding
26	28	Hapmap43005-BTA-64606	45805105	28	45772443	45814853	42411	-	ENSBTAG00000020314	COG2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40198
2	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40199
3	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40200
4	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40201
5	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40202
6	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40203
7	3	ARS-BFGL-NGS-4585	107160292	Production	107191525	107191529	40204
8	3	ARS-BFGL-NGS-4585	107160292	Production	107191525	107191529	40205
9	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40206
10	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40207
11	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40208
12	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40209
13	3	ARS-BFGL-NGS-4585	107160292	Health	107191525	107191529	40210
14	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40211
15	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40212
16	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40213
17	11	BTA-122098-no-rs	31882807	Milk	31870044	31870048	122275
18	14	Hapmap39823-BTA-35254	64061208	Milk	64013785	64013789	247433
19	14	Hapmap39823-BTA-35254	64061208	Milk	64013858	64013862	241580
20	14	Hapmap39823-BTA-35254	64061208	Milk	64013858	64013862	252031
21	14	Hapmap39823-BTA-35254	64061208	Milk	64013897	64013901	241079
22	14	Hapmap39823-BTA-35254	64061208	Milk	64013897	64013901	251434
23	14	Hapmap39823-BTA-35254	64061208	Milk	64013952	64013956	241853
24	14	Hapmap39823-BTA-35254	64061208	Milk	64013952	64013956	252403
25	14	Hapmap39823-BTA-35254	64061208	Milk	64015726	64015730	105628
26	14	Hapmap39823-BTA-35254	64061208	Milk	64017424	64017428	252151
27	14	Hapmap39823-BTA-35254	64061208	Milk	64017970	64017974	18873
28	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	18872
29	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	105335
30	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	173792
31	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	175101
32	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	176088
33	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	176487
34	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	252723
35	14	Hapmap39823-BTA-35254	64061208	Milk	64063422	64063426	175102
36	14	Hapmap39823-BTA-35254	64061208	Milk	64068329	64068333	105336
37	14	Hapmap39823-BTA-35254	64061208	Milk	64081490	64081494	104637
38	14	Hapmap39823-BTA-35254	64061208	Milk	64081490	64081494	104890
39	14	Hapmap39823-BTA-35254	64061208	Health	64081490	64081494	179830
40	14	Hapmap39823-BTA-35254	64061208	Milk	64087234	64087238	175103
41	14	Hapmap39823-BTA-35254	64061208	Milk	64087234	64087238	252869
42	14	Hapmap39823-BTA-35254	64061208	Milk	64087450	64087454	252628
43	14	Hapmap39823-BTA-35254	64061208	Milk	64088569	64088573	252926
44	14	Hapmap39823-BTA-35254	64061208	Milk	64103583	64103587	104638
45	14	Hapmap39823-BTA-35254	64061208	Milk	64103583	64103587	104891
46	14	UA-IFASA-7664	64299714	Milk	64272869	64272873	102476
47	14	UA-IFASA-7664	64299714	Milk	64272869	64272873	104484
48	14	UA-IFASA-7664	64299714	Milk	64272869	64272873	104863
49	14	UA-IFASA-7664	64299714	Health	64272869	64272873	179764
50	14	UA-IFASA-7664	64299714	Milk	64283263	64283267	175108
51	14	UA-IFASA-7664	64299714	Milk	64283263	64283267	251800
52	14	UA-IFASA-7664	64299714	Milk	64283947	64283951	251398
53	14	UA-IFASA-7664	64299714	Milk	64288968	64288972	247633
54	14	UA-IFASA-7664	64299714	Milk	64288968	64288972	252142
55	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	18875
56	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	105660
57	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	158137
58	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	173793
59	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	175109
60	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	176085
61	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	176486
62	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	250009
63	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	252724
64	14	UA-IFASA-7664	64299714	Milk	64302757	64302761	251934
65	14	UA-IFASA-7664	64299714	Milk	64308608	64308612	252948
66	14	UA-IFASA-7664	64299714	Milk	64317001	64317005	252652
67	14	UA-IFASA-7664	64299714	Milk	64322496	64322500	251752
68	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20269
69	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20311
70	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20393
71	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20464
72	14	UA-IFASA-7664	64299714	Milk	64327904	64327908	32596
73	14	UA-IFASA-7664	64299714	Milk	64327904	64327908	32634
74	14	UA-IFASA-7664	64299714	Milk	64328322	64328326	252206
75	14	UA-IFASA-7664	64299714	Milk	64339118	64339122	251497
76	14	UA-IFASA-7664	64299714	Milk	64343288	64343292	252287
77	19	ARS-BFGL-NGS-28901	42338148	Milk	42292941	42292945	147657
78	19	ARS-BFGL-NGS-28901	42338148	Milk	42292941	42292945	147675
79	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173073
80	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173074
81	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173075
82	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173076
83	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173077
84	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173237
85	19	ARS-BFGL-NGS-28901	42338148	Milk	42338146	42338150	166545
86	19	ARS-BFGL-NGS-28901	42338148	Milk	42338146	42338150	166546
87	19	ARS-BFGL-NGS-28901	42338148	Milk	42338146	42338150	253605
88	19	ARS-BFGL-NGS-28901	42338148	Milk	42349650	42349654	147630
89	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	195323
90	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	195324
91	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	195325
92	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	205408
93	19	ARS-BFGL-NGS-29119	49744592	Meat_and_Carcass	49715846	49715850	107754
94	19	ARS-BFGL-NGS-29119	49744592	Milk	49735877	49735881	196362
95	19	ARS-BFGL-NGS-29119	49744592	Milk	49772086	49772090	195342
96	20	BTB-01471673	11545685	Production	11498161	11498165	65554
97	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
98	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
99	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601
100	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51802
101	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51803
102	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51804
103	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51805
104	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51806
105	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51807
106	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51808
107	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51809
108	22	ARS-BFGL-NGS-26419	46052082	Health	46052080	46052084	51810
109	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51811
110	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51812
111	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51813
112	22	ARS-BFGL-NGS-26419	46052082	Milk	46073517	46073521	243365
113	26	BTB-00926636	17857480	Milk	17809768	17809772	195264
114	26	BTB-00926636	17857480	Milk	17809768	17809772	200269
115	26	BTB-00926636	17857480	Milk	17809768	17809772	200270
116	26	BTB-00926636	17857480	Milk	17809768	17809772	205295
117	26	BTB-00926636	17857480	Milk	17810363	17810367	198859
118	26	BTB-00926636	17857480	Milk	17810363	17810367	203868
119	26	BTB-00926636	17857480	Milk	17810363	17810367	203869
120	26	BTB-00926636	17857480	Milk	17810363	17810367	210297
121	26	BTB-00926636	17857480	Milk	17811021	17811025	198858
122	26	BTB-00926636	17857480	Milk	17811021	17811025	203866
123	26	BTB-00926636	17857480	Milk	17811021	17811025	203867
124	26	BTB-00926636	17857480	Milk	17811021	17811025	210296
125	26	BTB-00926636	17857480	Milk	17811725	17811729	197493
126	26	BTB-00926636	17857480	Milk	17811725	17811729	202704
127	26	BTB-00926636	17857480	Milk	17812273	17812277	195685
128	26	BTB-00926636	17857480	Milk	17812273	17812277	200785
129	26	BTB-00926636	17857480	Milk	17813212	17813216	195285
130	26	BTB-00926636	17857480	Milk	17813212	17813216	200301
131	26	BTB-00926636	17857480	Milk	17813978	17813982	196236
132	26	BTB-00926636	17857480	Milk	17813978	17813982	201468
133	26	BTB-00926636	17857480	Milk	17815237	17815241	198002
134	26	BTB-00926636	17857480	Milk	17815237	17815241	203190
135	26	BTB-00926636	17857480	Milk	17817223	17817227	34750
136	26	BTB-00926636	17857480	Milk	17817223	17817227	195586
137	26	BTB-00926636	17857480	Milk	17821297	17821301	197235
138	26	BTB-00926636	17857480	Milk	17821297	17821301	202455
139	26	BTB-00926636	17857480	Milk	17822209	17822213	197783
140	26	BTB-00926636	17857480	Milk	17822209	17822213	202983
141	26	BTB-00926636	17857480	Milk	17823105	17823109	196204
142	26	BTB-00926636	17857480	Milk	17823105	17823109	201419
143	26	BTB-00926636	17857480	Milk	17824770	17824774	196867
144	26	BTB-00926636	17857480	Milk	17824770	17824774	202101
145	26	BTB-00926636	17857480	Milk	17833551	17833555	195748
146	26	BTB-00926636	17857480	Milk	17833551	17833555	200855
147	26	BTB-00926636	17857480	Milk	17833551	17833555	206213
148	26	BTB-00926636	17857480	Milk	17865069	17865073	197144
149	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	197144
150	26	BTB-00926636	17857480	Milk	17865069	17865073	202367
151	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	202367
152	26	BTB-00926636	17857480	Milk	17865069	17865073	208485
153	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	208485
154	26	BTB-00926636	17857480	Milk	17871201	17871205	197611
155	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	197611
156	26	BTB-00926636	17857480	Milk	17871201	17871205	202816
157	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	202816
158	26	BTB-00926636	17857480	Milk	17871201	17871205	209076
159	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	209076
160	26	BTB-00926636	17857480	Milk	17885871	17885875	63656
161	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	63656
162	26	BTB-00926636	17857480	Milk	17885871	17885875	199068
163	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	199068
164	26	BTB-00926636	17857480	Milk	17885871	17885875	204113
165	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204113
166	26	BTB-00926636	17857480	Milk	17885871	17885875	204114
167	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204114
168	26	BTB-00926636	17857480	Milk	17885871	17885875	210399
169	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	210399
170	26	BTB-00926636	17857480	Milk	17895273	17895277	63657
171	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	63657
172	26	BTB-00926636	17857480	Milk	17895273	17895277	199069
173	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	199069
174	26	BTB-00926636	17857480	Milk	17895273	17895277	204115
175	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204115
176	26	BTB-00926636	17857480	Milk	17895273	17895277	204116
177	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204116
178	26	BTB-00926636	17857480	Milk	17895273	17895277	210400
179	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	210400
180	26	BTB-00926636	17857480	Milk	17895803	17895807	199070
181	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	199070
182	26	BTB-00926636	17857480	Milk	17895803	17895807	204117
183	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204117
184	26	BTB-00926636	17857480	Milk	17895803	17895807	204118
185	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204118
186	26	BTB-00926636	17857480	Milk	17895803	17895807	210401
187	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	210401
188	26	BTB-00926636	17857480	Milk	17900304	17900308	199071
189	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	199071
190	26	BTB-00926636	17857480	Milk	17900304	17900308	204119
191	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204119
192	26	BTB-00926636	17857480	Milk	17900304	17900308	204120
193	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204120
194	26	BTB-00926636	17857480	Milk	17900304	17900308	210402
195	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	210402
196	26	BTB-00926636	17857480	Milk	17902932	17902936	63658
197	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	63658
198	26	BTB-00926636	17857480	Milk	17902932	17902936	199072
199	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	199072
200	26	BTB-00926636	17857480	Milk	17902932	17902936	204121
201	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204121
202	26	BTB-00926636	17857480	Milk	17902932	17902936	204122
203	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204122
204	26	BTB-00926636	17857480	Milk	17902932	17902936	210403
205	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	210403
206	26	BTB-00926636	17857480	Milk	17906861	17906865	63660
207	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	63660
208	26	BTB-00926636	17857480	Milk	17906861	17906865	199074
209	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	199074
210	26	BTB-00926636	17857480	Milk	17906861	17906865	204125
211	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204125
212	26	BTB-00926636	17857480	Milk	17906861	17906865	204126
213	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204126
214	26	BTB-00926636	17857480	Milk	17906861	17906865	210405
215	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	210405
216	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	63654
217	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	199076
218	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204129
219	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204130
220	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	210407
221	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	63655
222	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	197826
223	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203026
224	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203027
225	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	209354
226	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	195860
227	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201008
228	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201009
229	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	206462
230	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	33984
231	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195668
232	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195669
233	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195670
234	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	200758
235	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	199092
236	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	204151
237	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107048
238	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107227
239	28	ARS-BFGL-NGS-11935	44942879	Reproduction	44971508	44971512	238444
240	28	ARS-BFGL-NGS-11935	44942879	Production	44981682	44981686	66703
241	28	ARS-BFGL-NGS-11935	44942879	Production	44981682	44981686	66704
242	28	ARS-BFGL-NGS-11935	44942879	Production	44981682	44981686	69429
243	28	Hapmap43005-BTA-64606	45805105	Milk	45811156	45811160	34467

20.1 QTL annotation on GALLO

QTL type

#GALLO
par(mar=c(8,20,8,8))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1)

My Image

QTL type

#GALLO
par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

Reproduction My Image

Health My Image

20.2 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_enrich_qtl_genome_name_15.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_enrich_qtl_genome_type_15.csv")

#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
15	Milk C14 index	35	4437	209	163224	0.0000000	0.0000000	Milk
28	Milk myristoleic acid content	26	3047	209	163224	0.0000000	0.0000000	Milk
31	Milk protein percentage	39	8803	209	163224	0.0000000	0.0000000	Milk
29	Milk palmitoleic acid content	15	2422	209	163224	0.0000007	0.0000079	Milk
16	Milk C16 index	12	2002	209	163224	0.0000129	0.0001159	Milk
36	Muscle carnosine content	3	67	209	163224	0.0000933	0.0006998	Meat and Carcass
23	Milk lactose content	3	105	209	163224	0.0003523	0.0022648	Milk
20	Milk fat content	2	92	209	163224	0.0063319	0.0356168	Milk
27	Milk myristic acid content	4	902	209	163224	0.0294859	0.1449683	Milk
33	Milk stearic acid content	2	218	209	163224	0.0322152	0.1449683	Milk
30	Milk pentadecylic acid content	2	280	209	163224	0.0505491	0.2067917	Milk
44	Teat length	2	300	209	163224	0.0570974	0.2141153	Exterior
14	Milk arachidic acid content	1	60	209	163224	0.0740084	0.2220251	Milk
25	Milk margaric acid content	1	59	209	163224	0.0728207	0.2220251	Milk
26	Milk mid-infrared spectra	1	55	209	163224	0.0680550	0.2220251	Milk
8	Ketosis	2	441	209	163224	0.1101333	0.2915293	Health
40	Rear leg placement - side view	2	430	209	163224	0.1056354	0.2915293	Exterior
39	Pregnancy rate	3	944	209	163224	0.1218012	0.3045029	Reproduction
37	Myristic acid content	1	120	209	163224	0.1425637	0.3376510	Meat and Carcass
6	Fat thickness at the 12th rib	1	169	209	163224	0.1947851	0.4382665	Meat and Carcass
7	Foot angle	2	672	209	163224	0.2129475	0.4563160	Exterior
13	Maturity rate	1	243	209	163224	0.2677107	0.5072331	Production
17	Milk C18 index	1	246	209	163224	0.2705243	0.5072331	Milk
42	Stillbirth	3	1363	209	163224	0.2544359	0.5072331	Reproduction
18	Milk capric acid content	2	912	209	163224	0.3259016	0.5640605	Milk
38	Net merit	2	903	209	163224	0.3216964	0.5640605	Production
24	Milk lauric acid content	1	366	209	163224	0.3746664	0.6021425	Milk
32	Milk protein yield	5	3093	209	163224	0.3633449	0.6021425	Milk
41	Somatic cell score	2	1122	209	163224	0.4213659	0.6538437	Health
5	Dairy form	1	514	209	163224	0.4829472	0.7244208	Exterior
3	Calving ease	5	3819	209	163224	0.5419946	0.7725995	Reproduction
9	Lean meat yield	1	621	209	163224	0.5494041	0.7725995	Meat and Carcass
43	Strength	1	664	209	163224	0.5736508	0.7813075	Exterior
45	Udder depth	1	695	209	163224	0.5903212	0.7813075	Exterior
19	Milk caprylic acid content	1	811	209	163224	0.6471450	0.8320436	Milk
10	Length of productive life	2	2004	209	163224	0.7280866	0.8855108	Production
35	Milking speed	1	1011	209	163224	0.7273020	0.8855108	Milk
2	Body weight gain	1	1354	209	163224	0.8248432	0.9440581	Production
4	Conception rate	1	1255	209	163224	0.8009513	0.9440581	Reproduction
11	Longissimus muscle area	1	1420	209	163224	0.8391628	0.9440581	Meat and Carcass
12	Marbling score	1	1817	209	163224	0.9037804	0.9830306	Meat and Carcass
34	Milk yield	5	6432	209	163224	0.9174952	0.9830306	Milk
1	Body weight	2	4289	209	163224	0.9746418	0.9967928	Production
21	Milk fat percentage	8	10941	209	163224	0.9726277	0.9967928	Milk
22	Milk fat yield	1	8220	209	163224	0.9999797	0.9999797	Milk

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	9	9077	209	163224	0.8264587	1
2	Health	4	5889	209	163224	0.9456284	1
3	Meat and Carcass	8	18258	209	163224	0.9999640	1
4	Milk	168	75352	209	163224	0.0000000	0
5	Production	8	19640	209	163224	0.9999916	1
6	Reproduction	12	35008	209	163224	1.0000000	1

My Image

21 GALLO - with Birth_Year and Sampling_Date

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.csv(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/SNP_sig_Bonf_ind_seg.csv")

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_genes2.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_genes2.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_qtl2.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_qtl_clean2.csv")

Dowloading the .gtf file from Ensembl https://useast.ensembl.org/info/data/ftp/index.html

Downloading the .gff file from AnimalQTLdb https://www.animalgenome.org/cgi-bin/QTLdb/index

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	22	ARS-BFGL-NGS-26419	46052082	22	45924535	46818511	893977	-	ENSBTAG00000013117	CACNA2D3	protein_coding
2	22	ARS-BFGL-NGS-26419	46052082	22	46051998	46062657	10660	+	ENSBTAG00000013124	LRTM1	protein_coding
3	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding
4	26	BTB-00926636	17857480	26	17703169	17846407	143239	-	ENSBTAG00000011743	TLL2	protein_coding
5	26	BTB-00926636	17857480	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
6	26	BTA-62125-no-rs	17899619	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
7	26	BTA-62125-no-rs	17899619	26	17925643	18029878	104236	-	ENSBTAG00000019872	PIK3AP1	protein_coding
8	28	BTA-99379-no-rs	41666186	28	41609015	41649016	40002	-	ENSBTAG00000007540	GLUD1	protein_coding
9	28	BTA-99379-no-rs	41666186	28	41650333	41763371	113039	+	ENSBTAG00000019194	SHLD2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
2	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
3	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601
4	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51802
5	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51803
6	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51804
7	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51805
8	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51806
9	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51807
10	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51808
11	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51809
12	22	ARS-BFGL-NGS-26419	46052082	Health	46052080	46052084	51810
13	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51811
14	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51812
15	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51813
16	22	ARS-BFGL-NGS-26419	46052082	Milk	46073517	46073521	243365
17	26	BTB-00926636	17857480	Milk	17809768	17809772	195264
18	26	BTB-00926636	17857480	Milk	17809768	17809772	200269
19	26	BTB-00926636	17857480	Milk	17809768	17809772	200270
20	26	BTB-00926636	17857480	Milk	17809768	17809772	205295
21	26	BTB-00926636	17857480	Milk	17810363	17810367	198859
22	26	BTB-00926636	17857480	Milk	17810363	17810367	203868
23	26	BTB-00926636	17857480	Milk	17810363	17810367	203869
24	26	BTB-00926636	17857480	Milk	17810363	17810367	210297
25	26	BTB-00926636	17857480	Milk	17811021	17811025	198858
26	26	BTB-00926636	17857480	Milk	17811021	17811025	203866
27	26	BTB-00926636	17857480	Milk	17811021	17811025	203867
28	26	BTB-00926636	17857480	Milk	17811021	17811025	210296
29	26	BTB-00926636	17857480	Milk	17811725	17811729	197493
30	26	BTB-00926636	17857480	Milk	17811725	17811729	202704
31	26	BTB-00926636	17857480	Milk	17812273	17812277	195685
32	26	BTB-00926636	17857480	Milk	17812273	17812277	200785
33	26	BTB-00926636	17857480	Milk	17813212	17813216	195285
34	26	BTB-00926636	17857480	Milk	17813212	17813216	200301
35	26	BTB-00926636	17857480	Milk	17813978	17813982	196236
36	26	BTB-00926636	17857480	Milk	17813978	17813982	201468
37	26	BTB-00926636	17857480	Milk	17815237	17815241	198002
38	26	BTB-00926636	17857480	Milk	17815237	17815241	203190
39	26	BTB-00926636	17857480	Milk	17817223	17817227	34750
40	26	BTB-00926636	17857480	Milk	17817223	17817227	195586
41	26	BTB-00926636	17857480	Milk	17821297	17821301	197235
42	26	BTB-00926636	17857480	Milk	17821297	17821301	202455
43	26	BTB-00926636	17857480	Milk	17822209	17822213	197783
44	26	BTB-00926636	17857480	Milk	17822209	17822213	202983
45	26	BTB-00926636	17857480	Milk	17823105	17823109	196204
46	26	BTB-00926636	17857480	Milk	17823105	17823109	201419
47	26	BTB-00926636	17857480	Milk	17824770	17824774	196867
48	26	BTB-00926636	17857480	Milk	17824770	17824774	202101
49	26	BTB-00926636	17857480	Milk	17833551	17833555	195748
50	26	BTB-00926636	17857480	Milk	17833551	17833555	200855
51	26	BTB-00926636	17857480	Milk	17833551	17833555	206213
52	26	BTB-00926636	17857480	Milk	17865069	17865073	197144
53	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	197144
54	26	BTB-00926636	17857480	Milk	17865069	17865073	202367
55	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	202367
56	26	BTB-00926636	17857480	Milk	17865069	17865073	208485
57	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	208485
58	26	BTB-00926636	17857480	Milk	17871201	17871205	197611
59	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	197611
60	26	BTB-00926636	17857480	Milk	17871201	17871205	202816
61	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	202816
62	26	BTB-00926636	17857480	Milk	17871201	17871205	209076
63	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	209076
64	26	BTB-00926636	17857480	Milk	17885871	17885875	63656
65	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	63656
66	26	BTB-00926636	17857480	Milk	17885871	17885875	199068
67	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	199068
68	26	BTB-00926636	17857480	Milk	17885871	17885875	204113
69	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204113
70	26	BTB-00926636	17857480	Milk	17885871	17885875	204114
71	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204114
72	26	BTB-00926636	17857480	Milk	17885871	17885875	210399
73	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	210399
74	26	BTB-00926636	17857480	Milk	17895273	17895277	63657
75	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	63657
76	26	BTB-00926636	17857480	Milk	17895273	17895277	199069
77	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	199069
78	26	BTB-00926636	17857480	Milk	17895273	17895277	204115
79	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204115
80	26	BTB-00926636	17857480	Milk	17895273	17895277	204116
81	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204116
82	26	BTB-00926636	17857480	Milk	17895273	17895277	210400
83	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	210400
84	26	BTB-00926636	17857480	Milk	17895803	17895807	199070
85	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	199070
86	26	BTB-00926636	17857480	Milk	17895803	17895807	204117
87	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204117
88	26	BTB-00926636	17857480	Milk	17895803	17895807	204118
89	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204118
90	26	BTB-00926636	17857480	Milk	17895803	17895807	210401
91	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	210401
92	26	BTB-00926636	17857480	Milk	17900304	17900308	199071
93	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	199071
94	26	BTB-00926636	17857480	Milk	17900304	17900308	204119
95	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204119
96	26	BTB-00926636	17857480	Milk	17900304	17900308	204120
97	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204120
98	26	BTB-00926636	17857480	Milk	17900304	17900308	210402
99	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	210402
100	26	BTB-00926636	17857480	Milk	17902932	17902936	63658
101	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	63658
102	26	BTB-00926636	17857480	Milk	17902932	17902936	199072
103	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	199072
104	26	BTB-00926636	17857480	Milk	17902932	17902936	204121
105	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204121
106	26	BTB-00926636	17857480	Milk	17902932	17902936	204122
107	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204122
108	26	BTB-00926636	17857480	Milk	17902932	17902936	210403
109	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	210403
110	26	BTB-00926636	17857480	Milk	17906861	17906865	63660
111	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	63660
112	26	BTB-00926636	17857480	Milk	17906861	17906865	199074
113	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	199074
114	26	BTB-00926636	17857480	Milk	17906861	17906865	204125
115	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204125
116	26	BTB-00926636	17857480	Milk	17906861	17906865	204126
117	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204126
118	26	BTB-00926636	17857480	Milk	17906861	17906865	210405
119	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	210405
120	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	63654
121	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	199076
122	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204129
123	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204130
124	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	210407
125	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	63655
126	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	197826
127	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203026
128	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203027
129	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	209354
130	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	195860
131	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201008
132	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201009
133	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	206462
134	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	33984
135	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195668
136	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195669
137	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195670
138	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	200758
139	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	199092
140	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	204151
141	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107048
142	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107227

21.1 QTL annotation on GALLO

QTL type

#GALLO
par(mar=c(8,20,8,8))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1)

My Image

QTL type

#GALLO
par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

Reproduction My Image

Health My Image

21.2 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_enrich_qtl_genome_name_2.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_enrich_qtl_genome_type_2.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
5	Milk C14 index	35	4437	108	163224	0.0000000	0.0000000	Milk
9	Milk myristoleic acid content	26	3047	108	163224	0.0000000	0.0000000	Milk
10	Milk palmitoleic acid content	15	2422	108	163224	0.0000000	0.0000000	Milk
6	Milk C16 index	12	2002	108	163224	0.0000000	0.0000001	Milk
12	Muscle carnosine content	3	67	108	163224	0.0000131	0.0000497	Meat and Carcass
14	Pregnancy rate	2	944	108	163224	0.1296627	0.4105985	Reproduction
18	Teat length	1	300	108	163224	0.1802436	0.4892327	Exterior
15	Rear leg placement - side view	1	430	108	163224	0.2479752	0.5235032	Exterior
17	Stillbirth	2	1363	108	163224	0.2280294	0.5235032	Reproduction
3	Foot angle	1	672	108	163224	0.3596272	0.6162877	Exterior
7	Milk capric acid content	1	912	108	163224	0.4541067	0.6162877	Milk
8	Milk myristic acid content	1	902	108	163224	0.4504612	0.6162877	Milk
13	Net merit	1	903	108	163224	0.4508269	0.6162877	Production
19	Udder depth	1	695	108	163224	0.3693423	0.6162877	Exterior
16	Somatic cell score	1	1122	108	163224	0.5253603	0.6654564	Health
2	Conception rate	1	1255	108	163224	0.5656375	0.6716945	Reproduction
1	Calving ease	2	3819	108	163224	0.7219243	0.7776744	Reproduction
4	Length of productive life	1	2004	108	163224	0.7367441	0.7776744	Production
11	Milk yield	1	6432	108	163224	0.9870080	0.9870080	Milk

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	4	9077	108	163224	0.8569775	0.9999953
2	Health	1	5889	108	163224	0.9811250	0.9999953
3	Meat and Carcass	3	18258	108	163224	0.9997109	0.9999953
4	Milk	91	75352	108	163224	0.0000000	0.0000000
5	Production	2	19640	108	163224	0.9999848	0.9999953
6	Reproduction	7	35008	108	163224	0.9999953	0.9999953

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22 GPROFILER ON-LINE with BY and SD

From the online version of GPROFILER i got the following results.

Legend My Image

My Image

23 Haplotype Blocks

First I need to generate the .ped file

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file

plink \
    --bfile ${DATA} \
    --cow \
    --recode \
    --out ${RESULT}

Second, is necessary to convert the .ped file to Linkage format:

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/hapblock_in

plink \
    --file ${DATA} \
    --cow \
    --recode HV \
    --out ${RESULT}

The code above will generate a pair of files .ped and .info for each chromosome

I ran the haploview only for chromosome 03, 11, 14, 19, 20, 22, 23, 26 and 28 that are when the significant SNP are located.

As the LD Plot bring the SNP ordered, first I looked at /home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_15.csv, to know the SNP position, and then compare with the Check Markers tab in the haploview SNP position x SNP# in plot

My Image

VEP	rs	SNP	Chr	bp	A1	A2	Freq	Haploview.Order	Block
yes	rs109888380	ARS-BFGL-NGS-4585	3	107160292	A	C	0.3353170	1818
yes	rs41624254	BTA-122098-no-rs	11	31882807	A	C	0.4444440	674
no	rs42217767	BTB-01060598	11	32301594	C	A	0.4503970	680
yes	rs41632193	Hapmap39823-BTA-35254	14	64061208	A	C	0.2916670	1196
yes	rs41632223	UA-IFASA-7664	14	64299714	C	A	0.2678570	1200
yes	rs109751680	ARS-BFGL-NGS-28901	19	42338148	C	A	0.3710320	713
yes	rs41577586	ARS-BFGL-NGS-29119	19	49744592	A	C	0.4047620	849	Block 38
yes	rs42590312	BTB-01471673	20	11545685	A	C	0.2460320	232
yes	rs110031217	ARS-BFGL-NGS-23411	22	5134078	A	C	0.1865080	88
yes	rs110991998	ARS-BFGL-NGS-26419	22	46052082	A	C	0.0813492	762
no	rs29022819	Hapmap54016-rs29022819	23	33609998	C	A	0.1904760	534
yes	rs41644634	BTA-62125-no-rs	26	17899619	C	A	0.0257937	297	Block 10
yes	rs42089058	BTB-00926636	26	17857480	C	A	0.0257937	296	Block 10
yes	rs109578343	ARS-BFGL-NGS-11935	28	44942879	A	C	0.0297619	773
yes	rs41567074	BTA-99379-no-rs	28	41666186	A	C	0.0496032	701
yes	rs41648479	Hapmap43005-BTA-64606	28	45805105	A	C	0.0297619	791	Block 21

As can be observed in the column “Block”,four significant SNPs fell within a Haploblock.

rs41577586 (ARS-BFGL-NGS-29119) - chr 19 - order 849 - Block 38 rs41644634 (BTA-62125-no-rs) - chr 26 - order 297 - Block 10 rs42089058 (BTB-00926636) - chr 26 - order 296 - Block 10 rs41648479 (Hapmap43005-BTA-64606) - chr 28 - order 791 - Block 21

Chr 3 (SNP 1818) My Image

Chr 11 (SNP 674 and 680) My Image

Chr 14 (SNP 1196 and 1200) My Image

Chr 19 (SNP 713) My Image

Chr 19 (SNP 849) My Image

Chr 20 (SNP 232) My Image

Chr 22 (SNP 88) My Image

Chr 22 (SNP 762) My Image

Chr 23 (SNP 534) My Image

Chr 26 (SNP 296 and 297) My Image

Chr 28 (SNP 701) My Image

Chr 28 (SNP 773 and 791) My Image

24 GWAS - EXTREME PHENO - WITH BY and SD

24.1 Data preparation

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

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pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv", header = T)
ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)


# Create an matrix with fixed effects with only those animals which also have phenotype and genotype
data_final$cdn_id <- ids_eq$cdn_id[match(data_final$ID, ids_eq$elora_id)]
fixeff <- data_final[,c("ID", "BIRTH_YEAR", "Sampling_date", "cdn_id")]
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id, ]
fixeff$FID <- "HO"
fixeff <- fixeff[, c("FID", "cdn_id", "BIRTH_YEAR", "Sampling_date")]

# Now we are gona remove the intermediary animals from pheno object
pheno$Categorical <- ifelse(pheno$cortisol >= 956, "H", 
                           ifelse(pheno$cortisol <= 190.8, "L", "M"))
table(pheno$Categorical)
pheno <- pheno[pheno$Categorical != "M", ]
pheno <- pheno[, c("FID", "cdn_id", "cortisol")]

# Now we are going to remove from fixeff the animals which are not in pheno
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id,]

#Checking if match the animals and order
identical(fixeff$cdn_id, pheno$cdn_id)

#Creating a file with animals to keep in the genotype file, we will use it on Plink
to_keep_geno <- pheno[, c("FID", "cdn_id")]

write.table(fixeff, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt", quote = F, row.names = F, col.names = T)
write.table(pheno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt", quote = F, row.names = F, col.names = T)
write.table(to_keep_geno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt", quote = F, row.names = F, col.names = F)

We ended up with
H (Hight) = 34 animals
L (Low) = 37 animals Total = 71 animals

On Plink we will remove all individuals from genotype files that are classified as Medium, keeping only the High and Low

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
KEEP=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt

plink2 --bfile ${DATA} --keep ${KEEP} --chr-set 29 --make-bed --out ${RESULT}

24.2 GWAS on GCTA

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

After ran the GWAS above I got the following message from the GCTA:

Error: analysis stopped because more than half of the variance components are constrained. The result would be unreliable. You may have a try of the option –reml-no-constrain.

As we got this error message, we needed to solve this problem, and for that we used the whole sample size (252 individuals) to estimate the variance components in GCTA, and after this, using this variance components from the whole sample we perfomed ths GWAS with the subset of individuals (34 High + 37 Low = 71)

24.3 BLUPF90+ GWAS

To run ssGWAS on Blupf90+ suite, we will need 4 different softwares:

renum: just to renumerate the files and generate the renf90.par
preGSF90: just to perfome a quality control with different parameter from the default.
blupf90+: used to estimate VCE and generate Ainv and Ginv
postGSF90: perform GWAS

The tutorial for preGSF90 and postGSF90 we can find in the link bellow https://nce.ads.uga.edu/wiki/doku.php?id=readme.pregsf90#gwas_options_postgsf90

According to the BLUPF90+ tutorial:

ssGWAS is an iterative procedure with 2 steps:
0) Quality control
1) prediction of GEBV with ssGBLUP
2) prediction of SNP marker effects based on the GEBV

24.3.1 Files preparation

Preparing files to run Variance components estimation using REML with AI (Average Information) algorithm.

First you need to create a directory in your home directory, prepare and save the following files in:

Phenotype and Fixed effects file
Pedigree file
Genotype file
BlupF90+ executable file
RenumF90 executable file
preGSf90 executable file
postGSf90 executable file
Parameter file

24.3.2 Download the executable files

Download from this website https://nce.ads.uga.edu/html/projects/programs/Linux/64bit/:

BlupF90+ = we will use to estimate the Variance components and GEBVs
renumF90 = we will use to renumerate the files
preGSf90 = we will use to perform the Quality control
postGSf90 = we will use for GWAS

24.3.3 SNP MAP

mapfile <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/DGVsnpinfo.2404.ho", header = T)

colnames(mapfile)

colnames(mapfile) <- c("SNP_ID", "CHR", "POS")

mapfile <- mapfile[,c("CHR", "POS", "SNP_ID")]

write.table(mapfile, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/snpmap.txt", col.names = T, row.names = F, quote = F)

24.3.4 Parameter file for Quality Control

But, before running the GEBV we will first perform one additional step to “CLEAN” our genotypes. Actually BLUPF90 by default perform a data cleaning with pre set parameters, but as HWE is not used by default, we will perform this additional step.

The Parameter card for this step is the parameter bellow:

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renum_QC.par


DATAFILE
fenofix.txt
TRAITS
2
FIELDS_PASSED TO OUTPUT

WEIGHT(S)

RESIDUAL_VARIANCE
1.0
EFFECT
3 cross numer
EFFECT
4 cross alpha
EFFECT
1 cross alpha
RANDOM
animal
FILE
bruno_ped_iid_blup.txt
FILE_POS
1 2 3 0 0
SNP_FILE
bruno_gntps_iid
PED_DEPTH
0
(CO)VARIANCES
1.0
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

DATAFILE: bellow this line you need to inform the name of the file with phenotype and fixed effects. As before running BLUPF90 on server you are going to direct the terminal to the directory where all these files are placed you only need to inform the name.
TRAITS: below this line you need to inform which column are the phenotype date in the previous file, in this example, 2.
FIELDS_PASSED TO OUTPUT:
WEIGHT (S):
RESIDUAL VARIANCE: for the firs run you need to inform the value of 1.0, for the second you can pick the variance from the firs run’s output.
EFFECT: you will inform your first effect, in this example, Birth Year, which is in the column 3, and the word cross numer because is a number.
EFFECT: you should provide the next effect, in this example, sample date, as sample date has one non numeric character you should inform as cross alpha, in this example column 4.
EFFECT: now I’m providing my animal ID information, in this example column 1, and again cross alpha because has number and letters in the ID. I’m also informing that this effect is RANDOM, and that is my animal effect.
FILE: bellow this line I need to provide the pedigree file. Again, as I’m already in the directory which contain the pedigree file I only need to provide the file name.
FILE-POS: Here I’ll inform which columns should be considered in the pedigree file, in this situation, 1 2 3 0 0.
PED_DEPTH: Now we can inform the depth we want the software considers the pedigree, or if we leave 0 it will the maximum possible.
(CO) VARIANCES: Here you should provide the Variance/Co-variance matrix, like as for residual variance in the first run we set up to 0 in this example that we don´t have to imagine any co-variance, but if you know that exist variance among you effects you shoul set up XXX for ….
OPTION outcallrate: Save the call rate information on SNP markers in the file.
OPTION saveCleanSNPs: This option generates 4 new files. We assume snpfile as a marker file.
- snpfile_clean = new SNP marker file.
- snpfile_clean_XrefID = new cross-reference file.
- snpfile_SNPs_removed = a list of removed markers.
- snpfile_Animals_removed = a list of removed animals.
OPTION minfreq 0.01: Minimum allele frequency to retain the marker.
OPTION map_file snpmap.txt: This option will upload the SNP MAP
OPTION excludeCHR 30 31: This option will remove sexual chromosome that is the 30 and 31

To run any softwere from Blupf90 suit we will perfom always in this way:

Go to the server you wanna run this analysis, for instance, grand

ssh grand

Now go to the directory you have created to run this analysis where that set of files are placed.

cd /home/bambrozi/2_CORTISOL/GWAS/BLUPF90

Make the renumF90 and BlupF90+ files executables

chmod +x renumf90
chmod +x blupf90+

Run renumF90

./renumf90

When you run the code above, it will ask you the name of your parameter card, for this step is renum_QC.par.

The command above will generate couple files, among them renf90.par

We modified renf90.par in:

renf90_DataClean.par
renf90_VarCompEst.par

The parameter card to perform the Quality Control is: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renf90_DataClean.par

It will be run using the software pre preGS90 to generate the Clean Genotype and SNP_MAP files after Quality Control.

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        23 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   1.0000    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   1.0000    
OPTION SNP_file bruno_gntps_iid
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

The second parameter card used for Variance Components Estimation (VCE) is the following: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renf90_VarCompEst.par

It will be run using the software pre blupf90+ to generate the VCE.

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        23 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   1.0000    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   1.0000    
OPTION SNP_file bruno_gntps_iid_clean
OPTION no_quality_control
OPTION method VCE
OPTION origID
OPTION missing 9999
OPTION se_covar_function H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)

OPTION SNP_file bruno_gntps_iid_clean: we are going to inform the genotype file generated in the previous step (the Quality Control). Blup will create an file with the same name that the original genotype file, and add the sufix **_clean**
OPTION no_quality_control we need to set up this option because we performed Quality Control in the previous step and now we don’t need that Blupf90+ perform again. Blupf90+ by default perform quality control, so if we do not want, we need to specify.
OPTION method: VCE (Variance Component Estimation).
OPTION OrigID: this will keep the original ID informed.
OPTION missing 9999: you are informing that missing values will appear as 9999
OPTION se_covar_function: H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)
- H2_1: the name that your function will appear on the output files.
- g_3_3: you are asking for genetic variance estimation for the 3rd informed effect.
- **_1_1**: this effect is in the 1st column.
- /(g_3_3_1_1+r_1_1): to get the total phenotipic variance, you are summing to genetic variance the residual variance of the effect in column 1.

In the parameter card above, we remove the option for Quality Control and added options for Variance Components Estimation, for Missing data, for origID and for heritability estimation, but the MOST IMPORTANT PART is we need to change the OPTION SNP_FILE, replacing the original genotype file, for the “clean” version generated in the previous step.

The Variance Components will be placed in the file: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/blupf90.log

blupf90.log
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Now you should update you renf90_VarCompEst.par file with these informations from the .log file

Copy Residual Variance from blupf90.log and will paste on renf90_VarCompEst.par RANDOM_RESIDUAL_VALUES Copy Genetic variance for effect x from blupf90.log and will paste on renf90_VarCompEst.par (CO) VARIANCE

If the Residual Variance and Genetic variance for effect x didn’t change in your blupf90.log the analysis ended, but if this value vary, you should update again the renf90.par and run again blupf90+ until this values don’t change more.

blupf90.log
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Parameter card
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Now that we have the Variance components we go for the next step:

Prediction of SNP marker effects based on the GEBV
GWAS for High and Low cortisol animals

To do this, I’ll:

generate the Phenotype_FixEff file like bellow
generate the genotype file like bellow
Perform the QUALITY CONTROL for the new (71 samples) genotype file
Add the VCE from the 252 data set in the parameter card
Generate the GEBV
Run GWAS

24.4 Prediction of SNP marker effects based on the GEBV

24.4.1 GWAS for High and Low cortisol animals

Now we’ll run a new analysis using the Variance Components Estimation from the previous step to perform the GWAS.

To perform this first we need a Phenotype file, and a Genotype file only with the 71 animals (High=34 and Low=37)

24.4.1.1 Updating the files

24.4.1.1.1 Phenotype and Fixed Effects files

I used the code bellow to remove individuals with MEDIUM cortisol Phenotype

phenofix <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/fenofix.txt")


# Now we are gona remove the intermediary animals from pheno object
phenofix$Categorical <- ifelse(phenofix$V2 >= 956, "H", 
                            ifelse(phenofix$V2 <= 190.8, "L", "M"))
table(phenofix$Categorical)

phenofix <- phenofix[phenofix$Categorical != "M", ]

phenofix <- phenofix[, c("V1", "V2", "V3", "V4")]

write.table(phenofix, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/fenofix.txt", col.names = F, row.names = F, quote = F)

24.4.1.1.2 Genotype files

I used this command line bellow to remove the individuals with MEDIUM phenotypes.

awk 'NR==FNR{ids[$1]; next} $1 in ids' fenofix.txt bruno_gntps_iid > bruno_gntps_iid_71

24.4.1.2 Running renum_QC.par

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renum_QC.par

DATAFILE
fenofix.txt
TRAITS
2
FIELDS_PASSED TO OUTPUT

WEIGHT(S)

RESIDUAL_VARIANCE
77182
EFFECT
3 cross numer
EFFECT
4 cross alpha
EFFECT
1 cross alpha
RANDOM
animal
FILE
bruno_ped_iid_blup.txt
FILE_POS
1 2 3 0 0
SNP_FILE
bruno_gntps_iid_71
PED_DEPTH
0
(CO)VARIANCES
28212
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

We modified renf90.par in e copies:

renf90_DataClean.par
renf90_ssGWAS1_Ginv.par
renf90_ssGWAS2_SNPeff.par

24.4.1.3 Running renf90_DataClean.par for Quality Control

To run the parameter card bellow we are going to use the software presGSf90 /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renf90_DataClean.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

24.4.1.4 Running renf90_ssGWAS1_Ginv.par for Ginv estimation

May be necessary to run the command bellow on the server Setting the stack size to “unlimited” allows the program to allocate memory for these large structures without hitting stack limits. By removing stack size limits, BLUPF90 is less likely to encounter segmentation faults or memory allocation issues that arise when the stack space is insufficient for the computations needed.

ulimit -s unlimited

The parameter card bellow we are going to run using the software blupf90+:

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renf90_ssGWAS1_Ginv.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION no_quality_control
OPTION origID
OPTION missing 9999
OPTION saveGInverse
OPTION saveA22Inverse
OPTION snp_p_value

24.4.1.5 Running renf90_ssGWAS2_SNPeff.par for GWAS

The parameter card bellow we are going to run using the software postGSf90:

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION origID
OPTION no_quality_control
OPTION readGInverse
OPTION readA22Inverse
OPTION map_file snpmap.txt_clean
OPTION snp_p_value
OPTION Manhattan_plot_R
OPTION Manhattan_plot

This code will generate several files, among them:

chrsnp_pval:
- Column 1: trait
- Column 2: effect
- Column 3: -log10(p-value)
- Column 4: SNP
- Column 5: Chromosome
- Column 6: Position in bp
Pft1e3.R: a r-code to generate the Manhattan plot in R using the chrsnp_pval

24.4.1.6 Manhattan Plots for BLUPF90 GWAS

24.4.1.6.1 Genome Independent Segment

To make the Manhattan Plot considering Genome Independent Segment we should run the code bellow. This code has part of the code in the file Pft1e3.R

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)


setwd("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO")
# Read in and process data for Manhattan plot
yyy1 <- read.table("chrsnp_pval")
yyy <- yyy1[order(yyy1$V4), ]
zzz <- yyy[which(yyy$V1 == 1 & yyy$V2 == 3), ]
n <- nrow(zzz)
y <- zzz[, 4]
x <- zzz[, 3]
chr1 <- zzz[, 5]
chr <- NULL
pos <- NULL

for (i in unique(yyy$V5)) {
  zz <- yyy[yyy$V5 == i, ]
  key <- zz$V4
  medio <- round(nrow(zz) / 2, 0)
  z <- key[medio]
  pos <- c(pos, z)
}

chrn <- unique(yyy$V5)
one <- which(chr1 %% 4 == 0)
two <- which(chr1 %% 4 == 1)
three <- which(chr1 %% 4 == 2)
four <- which(chr1 %% 4 == 3)
chr[one] <- "darkgoldenrod"
chr[two] <- "darkorchid"
chr[three] <- "blue"
chr[four] <- "forestgreen"

# Create Manhattan plot with Bonferroni line and legend
pdf(file = "Pft1e3_manplot_with_bonf_ind_seg.pdf", family = "sans", height = 27.8, width = 50, pointsize = 20, bg = "white")
par(mfrow = c(1, 1), family = "sans", cex = 1.5, font = 2)
plot(y, x, xaxt = "n", main = "Manhattan Plot SNP p_value - Trait: 1 Effect: 3", xlab = "", ylab = "-log10(p-value)", pch = 20, xlim = c(1, n), ylim = c(0, max(x)), col = chr)

# Add Bonferroni line
abline(h = bonf, col = "red", lwd = 2, lty = 2)

# Add legend for Bonferroni line
legend("topright", legend = "Bonferroni correction for genome independent segments", col = "red", lwd = 2, lty = 2, cex = 1)

axis(1, at = pos, labels = chrn)
dev.off()

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For additional analysis like Variant Effect Prediction (VEP) we need the rsID, to get the rsID we use the software SNPChimp which requires SNP_names, but the BLUPF90 output have only the Chromosome and Position of the SNPs, so we are going to perfome these two steps to get one file with t he significant SNPs + SNP_name + rsID

24.4.1.6.1.1 Step 01 = Bring the SNP name to GWAS output

For this analysis we have to build this new sheet bringing SNP ID from snpmap.txt

gwas = read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval")
colnames(gwas) <- c("V1", "V2", "LOG_P", "SNP", "CHR", "BP")
gwas <- filter(gwas, LOG_P >= bonf)

snpmap <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/snpmap.txt", header = T)

# Filter snpmap to only include rows that match the CHR and BP values in out_genes
filtered_snpmap <- snpmap[snpmap$CHR %in% gwas$CHR & snpmap$POS %in% gwas$BP, ]

# Merge the filtered snpmap with out_genes
gwas_snpname <- merge(gwas, filtered_snpmap[, c("CHR", "POS", "SNP_ID")], 
                   by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), 
                   all.x = TRUE)

gwas_snpname <- gwas_snpname[,c("CHR", "BP", "LOG_P", "SNP_ID")]

write.csv(gwas_snpname, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval_SNPid_ind_seg_sig_BLUPF90.csv")

X	CHR	BP	LOG_P	SNP_ID	rsID	X.1	rs	SNP_name	chromosome	position
1	11	19779915	4.828190	Hapmap55558-rs29013980	NA	NA	rs110160157	ARS-BFGL-NGS-12139	0	0
2	12	17477322	4.417576	BTB-00488482	NA	NA	rs42751504	ARS-BFGL-NGS-37809	0	0
3	14	15929822	5.467439	ARS-BFGL-NGS-82859	NA	NA	rs43691687	BTB-00488482	0	0
4	15	33066384	5.041271	BTB-00594449	NA	NA	rs42305073	BTB-01148543	0	0
5	15	57930281	4.514377	ARS-BFGL-NGS-30515	NA	NA	rs29013980	Hapmap55558-rs29013980	11	19803705
6	17	46487068	4.547766	ARS-BFGL-NGS-12510	NA	NA	rs43691687	BTB-00488482	12	16563156
7	17	47884497	6.622354	ARS-BFGL-NGS-87412	NA	NA	rs110506037	ARS-BFGL-NGS-82859	14	15419973
8	17	48800954	5.268940	ARS-BFGL-NGS-112149	NA	NA	rs41764450	BTB-00594449	15	31708030
9	2	118820218	4.420962	Hapmap53065-rs29026778	NA	NA	rs41764450	BTB-00594449	15	33601229
10	2	41602429	4.497977	BTB-00096979	NA	NA	rs110565206	ARS-BFGL-NGS-30515	15	57514944
11	2	41670655	4.377284	Hapmap48777-BTA-47434	NA	NA	rs110565206	ARS-BFGL-NGS-30515	15	58709858
12	20	60365668	4.603817	BTB-01341053	NA	NA	rs110038841	ARS-BFGL-NGS-12510	17	47484553
13	20	65351653	4.891206	ARS-BFGL-NGS-91119	NA	NA	rs109273103	ARS-BFGL-NGS-87412	17	48889044
14	21	4055731	4.493938	ARS-BFGL-NGS-112210	NA	NA	rs110038841	ARS-BFGL-NGS-12510	17	48998486
15	23	44822126	4.316434	ARS-BFGL-NGS-115605	NA	NA	rs109276211	ARS-BFGL-NGS-112149	17	49828442
16	24	857728	4.458018	Hapmap47669-BTA-59022	NA	NA	rs109273103	ARS-BFGL-NGS-87412	17	50583981
17	28	20016672	5.130557	ARS-BFGL-NGS-116552	NA	NA	rs109276211	ARS-BFGL-NGS-112149	17	51512244
18	28	35807366	4.695875	ARS-BFGL-NGS-71077	NA	NA	rs43305418	BTB-00096979	2	41717655
19	29	33039863	4.933140	ARS-BFGL-NGS-41631	NA	NA	rs41636137	Hapmap48777-BTA-47434	2	41786376
20	3	110866523	4.815943	ARS-BFGL-NGS-118207	NA	NA	rs43305418	BTB-00096979	2	43072293
21	3	111526170	4.760351	ARS-BFGL-NGS-74948	NA	NA	rs41636137	Hapmap48777-BTA-47434	2	43141034
22	3	111730561	4.390707	BTB-01641394	NA	NA	rs29026778	Hapmap53065-rs29026778	2	119630462
23	3	111751663	4.390707	ARS-BFGL-NGS-37809	NA	NA	rs29026778	Hapmap53065-rs29026778	2	122995670
24	3	111772736	5.088738	ARS-BFGL-NGS-25298	NA	NA	rs42462935	BTB-01341053	20	60447656
25	4	103979600	5.591006	ARS-BFGL-NGS-110705	NA	NA	rs42462935	BTB-01341053	20	64066021
26	4	107787202	5.375628	ARS-BFGL-NGS-45265	NA	NA	rs109575643	ARS-BFGL-NGS-91119	20	65467968
27	4	109568557	4.709882	Hapmap48062-BTA-72409	NA	NA	rs109575643	ARS-BFGL-NGS-91119	20	69096095
28	4	110053134	5.245598	UA-IFASA-2147	NA	NA	rs109631116	ARS-BFGL-NGS-112210	21	2685189
29	4	24239851	4.641241	Hapmap60681-rs29013301	NA	NA	rs109631116	ARS-BFGL-NGS-112210	21	4193189
30	4	25933587	4.352451	Hapmap50554-BTA-107048	NA	NA	rs42029843	ARS-BFGL-NGS-115605	23	44680105
31	4	26511448	4.537662	Hapmap59011-rs29027498	NA	NA	rs42029843	ARS-BFGL-NGS-115605	23	45514921
32	4	27021431	5.045447	Hapmap59743-rs29017061	NA	NA	rs41645754	Hapmap47669-BTA-59022	24	595659
33	4	27094376	5.485063	BTB-00170785	NA	NA	rs41645754	Hapmap47669-BTA-59022	24	1094942
34	4	50755462	5.015905	ARS-BFGL-NGS-12139	NA	NA	rs110428837	ARS-BFGL-NGS-116552	28	18933788
35	4	7873471	4.610767	Hapmap60503-rs29018741	NA	NA	rs110428837	ARS-BFGL-NGS-116552	28	20096355
36	4	95650788	4.825103	Hapmap58854-rs29023486	NA	NA	rs109584097	ARS-BFGL-NGS-71077	28	35297722
37	6	36184467	4.758110	Hapmap23854-BTC-062412	NA	NA	rs109584097	ARS-BFGL-NGS-71077	28	36010482
38	7	110306791	4.609691	BTB-01148543	NA	NA	rs110121846	ARS-BFGL-NGS-41631	29	33593218
NA	NA	NA	NA		NA	NA	rs110121846	ARS-BFGL-NGS-41631	29	34563419
NA	NA	NA	NA		NA	NA	rs110081798	ARS-BFGL-NGS-118207	3	111458159
NA	NA	NA	NA		NA	NA	rs41585925	ARS-BFGL-NGS-74948	3	112135228
NA	NA	NA	NA		NA	NA	rs42752353	BTB-01641394	3	112340357
NA	NA	NA	NA		NA	NA	rs42751504	ARS-BFGL-NGS-37809	3	112361478
NA	NA	NA	NA		NA	NA	rs109868537	ARS-BFGL-NGS-25298	3	112382551
NA	NA	NA	NA		NA	NA	rs110081798	ARS-BFGL-NGS-118207	3	118126544
NA	NA	NA	NA		NA	NA	rs41585925	ARS-BFGL-NGS-74948	3	118803985
NA	NA	NA	NA		NA	NA	rs42752353	BTB-01641394	3	119008234
NA	NA	NA	NA		NA	NA	rs42751504	ARS-BFGL-NGS-37809	3	119029352
NA	NA	NA	NA		NA	NA	rs29018741	Hapmap60503-rs29018741	4	7779209
NA	NA	NA	NA		NA	NA	rs29018741	Hapmap60503-rs29018741	4	8585313
NA	NA	NA	NA		NA	NA	rs29013301	Hapmap60681-rs29013301	4	24183919
NA	NA	NA	NA		NA	NA	rs29013301	Hapmap60681-rs29013301	4	25355321
NA	NA	NA	NA		NA	NA	rs41615935	Hapmap50554-BTA-107048	4	25913130
NA	NA	NA	NA		NA	NA	rs29027498	Hapmap59011-rs29027498	4	26490406
NA	NA	NA	NA		NA	NA	rs29017061	Hapmap59743-rs29017061	4	26995149
NA	NA	NA	NA		NA	NA	rs43377276	BTB-00170785	4	27067897
NA	NA	NA	NA		NA	NA	rs41615935	Hapmap50554-BTA-107048	4	27162987
NA	NA	NA	NA		NA	NA	rs29027498	Hapmap59011-rs29027498	4	27732337
NA	NA	NA	NA		NA	NA	rs29017061	Hapmap59743-rs29017061	4	28239398
NA	NA	NA	NA		NA	NA	rs43377276	BTB-00170785	4	28312330
NA	NA	NA	NA		NA	NA	rs110160157	ARS-BFGL-NGS-12139	4	51047434
NA	NA	NA	NA		NA	NA	rs110160157	ARS-BFGL-NGS-12139	4	52798744
NA	NA	NA	NA		NA	NA	rs29023486	Hapmap58854-rs29023486	4	96436124
NA	NA	NA	NA		NA	NA	rs29023486	Hapmap58854-rs29023486	4	98848662
NA	NA	NA	NA		NA	NA	rs110079750	ARS-BFGL-NGS-110705	4	104816971
NA	NA	NA	NA		NA	NA	rs110079750	ARS-BFGL-NGS-110705	4	107675833
NA	NA	NA	NA		NA	NA	rs110935391	ARS-BFGL-NGS-45265	4	108608248
NA	NA	NA	NA		NA	NA	rs41566051	Hapmap48062-BTA-72409	4	110354858
NA	NA	NA	NA		NA	NA	rs29012492	UA-IFASA-2147	4	110839255
NA	NA	NA	NA		NA	NA	rs110935391	ARS-BFGL-NGS-45265	4	111954676
NA	NA	NA	NA		NA	NA	rs41566051	Hapmap48062-BTA-72409	4	113822987
NA	NA	NA	NA		NA	NA	rs29012492	UA-IFASA-2147	4	114307093
NA	NA	NA	NA		NA	NA	rs81154019	Hapmap23854-BTC-062412	6	37610880

24.4.1.6.1.2 Step 02 = Bring the rsID to the file with SNP name.

After get the rsID from SNPCHIMP I produced this table with the code bellow:

rsid <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/SNPchimp_result_1723415732_BLUPF90GWAS.tsv", header = T)

merged <- merge(rsid, gwas_snpname, by.x ="SNP_name", by.y ="SNP_ID")

colnames(merged)

merged <- merged[,c("SNP_name", "rs", "CHR", "BP", "LOG_P")]

colnames(merged) <- c("SNP_name", "rsID", "CHR", "BP", "LOG_P")

write.csv(merged, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")

X	SNP_name	rsID	CHR	BP	LOG_P
1	ARS-BFGL-NGS-110705	rs110079750	4	103979600	5.591006
2	ARS-BFGL-NGS-112149	rs109276211	17	48800954	5.268940
3	ARS-BFGL-NGS-112210	rs109631116	21	4055731	4.493938
4	ARS-BFGL-NGS-115605	rs42029843	23	44822126	4.316434
5	ARS-BFGL-NGS-116552	rs110428837	28	20016672	5.130557
6	ARS-BFGL-NGS-118207	rs110081798	3	110866523	4.815943
7	ARS-BFGL-NGS-12139	rs110160157	4	50755462	5.015905
8	ARS-BFGL-NGS-12510	rs110038841	17	46487068	4.547766
9	ARS-BFGL-NGS-25298	rs109868537	3	111772736	5.088738
10	ARS-BFGL-NGS-30515	rs110565206	15	57930281	4.514377
11	ARS-BFGL-NGS-37809	rs42751504	3	111751663	4.390707
12	ARS-BFGL-NGS-41631	rs110121846	29	33039863	4.933140
13	ARS-BFGL-NGS-45265	rs110935391	4	107787202	5.375628
14	ARS-BFGL-NGS-71077	rs109584097	28	35807366	4.695875
15	ARS-BFGL-NGS-74948	rs41585925	3	111526170	4.760351
16	ARS-BFGL-NGS-82859	rs110506037	14	15929822	5.467439
17	ARS-BFGL-NGS-87412	rs109273103	17	47884497	6.622354
18	ARS-BFGL-NGS-91119	rs109575643	20	65351653	4.891206
19	BTB-00096979	rs43305418	2	41602429	4.497977
20	BTB-00170785	rs43377276	4	27094376	5.485063
21	BTB-00488482	rs43691687	12	17477322	4.417576
22	BTB-00594449	rs41764450	15	33066384	5.041271
23	BTB-01148543	rs42305073	7	110306791	4.609691
24	BTB-01341053	rs42462935	20	60365668	4.603817
25	BTB-01641394	rs42752353	3	111730561	4.390707
26	Hapmap23854-BTC-062412	rs81154019	6	36184467	4.758110
27	Hapmap47669-BTA-59022	rs41645754	24	857728	4.458018
28	Hapmap48062-BTA-72409	rs41566051	4	109568557	4.709882
29	Hapmap48777-BTA-47434	rs41636137	2	41670655	4.377284
30	Hapmap50554-BTA-107048	rs41615935	4	25933587	4.352451
31	Hapmap53065-rs29026778	rs29026778	2	118820218	4.420962
32	Hapmap55558-rs29013980	rs29013980	11	19779915	4.828190
33	Hapmap58854-rs29023486	rs29023486	4	95650788	4.825103
34	Hapmap59011-rs29027498	rs29027498	4	26511448	4.537662
35	Hapmap59743-rs29017061	rs29017061	4	27021431	5.045447
36	Hapmap60503-rs29018741	rs29018741	4	7873471	4.610767
37	Hapmap60681-rs29013301	rs29013301	4	24239851	4.641241
38	UA-IFASA-2147	rs29012492	4	110053134	5.245598

24.4.1.7 WINDOWS

24.4.1.8 Running renf90_ssGWAS2_SNPeff_w.par for GWAS (WINDOWS)

The parameter card bellow we are going to run using the software postGSf90:

renf90_ssGWAS2_SNPeff_W_10.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION origID
OPTION no_quality_control
OPTION readGInverse
OPTION readA22Inverse
OPTION map_file snpmap.txt_clean
OPTION snp_p_value
OPTION Manhattan_plot_R
OPTION Manhattan_plot
OPTION SNP_moving_average 10
OPTION windows_variance 10

The parameter file above will generate the following files:

snp_sol
- column 1: trait
- column 2: effect
- column 3: SNP
- column 4: Chromosome
- column 5: Position
- column 6: SNP solution
- column 7: weight
- column 8: variance explained by n adjacents SNP (if OPTION windows_variance is used).
- column 9: variance of the SNP solution (used to compute the p-value) (if OPTION snp_p_value is used)
snp_pred
- contains allele frequencies + SNP effects
chrsnpvar
- column 1: trait
- column 2: effect
- column 3: variance explained by n adjacents SNP
- column 4: SNP
- column 5: Chromosome
- column 6: Position
chrsnp_pval
- column 1: trait
- column 2: effect
- column 3: -log10(p-value)
- column 4: SNP
- column 5: Chromosome
- column 6: Position in bp
chrsnp
- column 1: trait
- column 2: effect
- column 3: values of SNP effects to use in Manhattan plots → [abs(SNP_i)/SD(SNP)]
- column 4: SNP
- column 5: Chromosome
- column 6: Position
windows_segments
- column 1: label
- column 2: window size (number of SNP)
- column 3: Start SNP number for the window
- column 4: End SNP number for the window
- column 5: identification of window: (ChrNumber)’_’(startPositionMBP)
- column 6: Start (ChrNumber)’_’(Position) for the window
- column 7: End (ChrNumber)’_’(Position) for the window
windows_variance
- column 1: trait
- column 2: effect
- column 3: Start SNP number or SNP name for the window
- column 4: End SNP number or SNP name for the window
- column 5: window size (number of SNP)
- column 6: Start (ChrNumber)’_’(Position) for the window
- column 7: End (ChrNumber)’_’(Position) for the window
- column 8: identification of window: (ChrNumber)’_’(startPositionMBP)
- column 9: variance explained by n adjacents SNP
Vft1e3.R
Sft1e3.R
Pft1e3.R

24.5 Match Significant p-value and Windows variance bigger than 0.5%

#Only significant SNPs after Genome Independent Segments adjust
sigPvalue <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval_SNPid_ind_seg_sig_BLUPF90.csv")
sigPvalue <- sigPvalue[, c("CHR", "BP", "LOG_P", "SNP_ID")]

w_var <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/chrsnpvar", header = F)
w_var <- filter(w_var, V3 > 0.5)
colnames(w_var) <- c("V1", "V2", "Var", "SNP", "CHR", "BP")

snp_map <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header = T)

# Fazer o merge baseado em duas condições: CHR e POS
merged_data <- merge(w_var, snp_map, by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), all.x = TRUE)
w_var <- merged_data[,c("CHR", "BP", "Var", "SNP_ID")]

# Get indices in w_var where SNP_ID matches those in sigPvalue
indices <- which(w_var$SNP_ID %in% sigPvalue$SNP_ID)

# Optionally extract the rows from w_var based on these indices
matching_rows <- w_var[indices, ]

write.csv(matching_rows, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/pvalue_windowsVar_match.csv")
write.csv(w_var, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/windowsVar_min05.csv")

sum(matching_rows$Var)

The 3 SNPs bellow were significant associated to cortisol considering the p-value adjusted for genome independent segments and also individually explain more than 0.5% of the variance.

X	CHR	BP	Var	SNP_ID
36	3	111730561	0.9480214	BTB-01641394
37	3	111751663	1.0607739	ARS-BFGL-NGS-37809
38	3	111772736	1.2101267	ARS-BFGL-NGS-25298

These 3 SNPs explain together 3,22% of Variance and all of them fall in the gene CSMD2

Next we need to check the allele frequency of this SNPs.

24.6 BLUPF90+ GALLO

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)


# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas = read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval")
colnames(gwas) <- c("V1", "V2", "LOG_P", "SNP", "CHR", "BP")
gwas <- filter(gwas, LOG_P >= bonf)


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_genes_50k.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_genes_50k.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_qtl_50k.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_qtl_50k_clean.csv")

The GALLO output are bellow:

For GENES

X	V1	V2	LOG_P	SNP	CHR	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	1	3	4.420962	4632	2	118820218	2	118803760	118824701	20942	+	ENSBTAG00000030718	SPATA3	protein_coding
2	1	3	4.420962	4632	2	118820218	2	118831062	118836777	5716	+	ENSBTAG00000054626	NA	lncRNA
3	1	3	4.420962	4632	2	118820218	2	118846915	118854132	7218	+	ENSBTAG00000026710	C2H2orf72	protein_coding
4	1	3	4.420962	4632	2	118820218	2	118865174	118946608	81435	+	ENSBTAG00000005119	PSMD1	protein_coding
5	1	3	4.377284	3498	2	41670655	2	41676135	42259624	583490	-	ENSBTAG00000005562	GALNT13	protein_coding
6	1	3	4.815943	6862	3	110866523	3	110769753	110827938	58186	+	ENSBTAG00000013867	DLGAP3	protein_coding
7	1	3	4.815943	6862	3	110866523	3	110835276	110839536	4261	+	ENSBTAG00000014235	SMIM12	protein_coding
8	1	3	4.815943	6862	3	110866523	3	110888198	110890904	2707	-	ENSBTAG00000013881	GJA4	protein_coding
9	1	3	4.815943	6862	3	110866523	3	110900183	110905849	5667	-	ENSBTAG00000012584	GJB3	protein_coding
10	1	3	4.760351	6873	3	111526170	3	111552563	111568286	15724	-	ENSBTAG00000020798	C3H1orf94	protein_coding
11	1	3	4.390707	6880	3	111730561	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
12	1	3	4.390707	6881	3	111751663	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
13	1	3	5.088738	6882	3	111772736	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
14	1	3	4.537662	7515	4	26511448	4	26324012	26469689	145678	-	ENSBTAG00000014074	SNX13	protein_coding
15	1	3	5.485063	7526	4	27094376	4	27093041	27667961	574921	+	ENSBTAG00000003808	HDAC9	protein_coding
16	1	3	4.825103	8704	4	95650788	4	95631850	95631921	72	-	ENSBTAG00000045386	bta-mir-320b	miRNA
17	1	3	5.591006	8831	4	103979600	4	103877533	103947944	70412	-	ENSBTAG00000004799	DENND2A	protein_coding
18	1	3	5.591006	8831	4	103979600	4	103994401	104008963	14563	+	ENSBTAG00000020484	ADCK2	protein_coding
19	1	3	5.591006	8831	4	103979600	4	104013524	104019157	5634	+	ENSBTAG00000021759	NDUFB2	protein_coding
20	1	3	5.591006	8831	4	103979600	4	104027627	104194547	166921	-	ENSBTAG00000021761	BRAF	protein_coding
21	1	3	5.375628	8888	4	107787202	4	107701708	108020989	319282	+	ENSBTAG00000054424	NA	protein_coding
22	1	3	5.375628	8888	4	107787202	4	107718449	108101163	382715	-	ENSBTAG00000017676	TPK1	protein_coding
23	1	3	4.709882	8904	4	109568557	4	109526182	109526350	169	-	ENSBTAG00000053666	NA	misc_RNA
24	1	3	4.610767	7184	4	7873471	4	7867490	7867588	99	-	ENSBTAG00000045389	U6	snRNA
25	1	3	5.015905	7910	4	50755462	4	50743789	50957593	213805	-	ENSBTAG00000006589	CFTR	protein_coding
26	1	3	4.758110	11511	6	36184467	6	36052150	36198478	146329	-	ENSBTAG00000010120	HERC3	protein_coding
27	1	3	4.609691	14772	7	110306791	7	110296879	110565532	268654	+	ENSBTAG00000035662	CAMK4	protein_coding
28	1	3	4.828190	20558	11	19779915	11	19680235	19765027	84793	-	ENSBTAG00000001114	PRKD3	protein_coding
29	1	3	4.828190	20558	11	19779915	11	19792170	19822758	30589	+	ENSBTAG00000013923	QPCT	protein_coding
30	1	3	5.467439	25167	14	15929822	14	15865228	15995482	130255	-	ENSBTAG00000013537	FER1L6	protein_coding
31	1	3	5.467439	25167	14	15929822	14	15914441	15917042	2602	+	ENSBTAG00000048373	NA	protein_coding
32	1	3	5.041271	26782	15	33066384	15	32820702	33050885	230184	-	ENSBTAG00000054820	NA	lncRNA
33	1	3	4.547766	29742	17	46487068	17	46406767	46715519	308753	+	ENSBTAG00000009797	RIMBP2	protein_coding
34	1	3	6.622354	29772	17	47884497	17	47426664	48084896	658233	+	ENSBTAG00000002950	TMEM132D	protein_coding
35	1	3	5.268940	29784	17	48800954	17	48417339	48868560	451222	-	ENSBTAG00000046256	TMEM132C	protein_coding
36	1	3	4.891206	33665	20	65351653	20	65311530	65343117	31588	-	ENSBTAG00000009401	MTRR	protein_coding
37	1	3	4.891206	33665	20	65351653	20	65343172	65354775	11604	+	ENSBTAG00000009400	FASTKD3	protein_coding
38	1	3	4.891206	33665	20	65351653	20	65368557	65384042	15486	+	ENSBTAG00000055034	CFAP90	protein_coding
39	1	3	4.891206	33665	20	65351653	20	65388705	65842986	454282	-	ENSBTAG00000019210	ADCY2	protein_coding
40	1	3	4.493938	33836	21	4055731	21	3866347	4146441	280095	-	ENSBTAG00000013422	GABRB3	protein_coding
41	1	3	4.316434	36688	23	44822126	23	44802116	44835671	33556	-	ENSBTAG00000010256	TMEM170B	protein_coding
42	1	3	4.458018	36876	24	857728	24	779627	866891	87265	-	ENSBTAG00000000656	NFATC1	protein_coding
43	1	3	4.458018	36876	24	857728	24	879074	1029386	150313	-	ENSBTAG00000001224	ATP9B	protein_coding
44	1	3	4.695875	41057	28	35807366	28	35805434	35823873	18440	+	ENSBTAG00000021416	PRXL2A	protein_coding
45	1	3	4.695875	41057	28	35807366	28	35848163	35904370	56208	+	ENSBTAG00000003907	TSPAN14	protein_coding
46	1	3	4.933140	41785	29	33039863	29	32993975	33024632	30658	-	ENSBTAG00000003176	JAM3	protein_coding
47	1	3	4.933140	41785	29	33039863	29	33036823	33044208	7386	-	ENSBTAG00000052082	NA	lncRNA

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	2	3497	41602429	Health	41584083	41584087	32371
2	2	3497	41602429	Milk	41646649	41646653	113699
3	2	3498	41670655	Milk	41646649	41646653	113699
4	2	3498	41670655	Milk	41668170	41668174	113700
5	2	3498	41670655	Meat_and_Carcass	41676143	41676147	154062
6	2	3498	41670655	Reproduction	41693862	41693866	29899
7	2	4632	118820218	Milk	118779183	118779187	200902
8	2	4632	118820218	Milk	118779985	118779989	202500
9	2	4632	118820218	Milk	118781186	118781190	202905
10	2	4632	118820218	Milk	118785252	118785256	201638
11	2	4632	118820218	Milk	118789209	118789213	202885
12	2	4632	118820218	Reproduction	118820216	118820220	39599
13	2	4632	118820218	Reproduction	118820216	118820220	39600
14	2	4632	118820218	Reproduction	118820216	118820220	39601
15	2	4632	118820218	Exterior	118820216	118820220	39602
16	2	4632	118820218	Exterior	118820216	118820220	39603
17	2	4632	118820218	Milk	118820216	118820220	39604
18	2	4632	118820218	Production	118820216	118820220	39605
19	2	4632	118820218	Exterior	118820216	118820220	39606
20	2	4632	118820218	Milk	118820216	118820220	39607
21	2	4632	118820218	Production	118820216	118820220	39608
22	2	4632	118820218	Production	118820216	118820220	39609
23	2	4632	118820218	Exterior	118820216	118820220	39610
24	2	4632	118820218	Exterior	118820216	118820220	39611
25	2	4632	118820218	Production	118820216	118820220	39612
26	2	4632	118820218	Reproduction	118820216	118820220	39613
27	2	4632	118820218	Health	118820216	118820220	39614
28	2	4632	118820218	Exterior	118820216	118820220	39615
29	2	4632	118820218	Exterior	118820216	118820220	39616
30	2	4632	118820218	Reproduction	118820216	118820220	125235
31	3	6862	110866523	Meat_and_Carcass	110866521	110866525	152332
32	3	6873	111526170	Health	111487002	111487006	137202
33	3	6873	111526170	Health	111487002	111487006	170274
34	3	6873	111526170	Health	111509252	111509256	170275
35	3	6873	111526170	Health	111515878	111515882	170276
36	3	6880	111730561	Reproduction	111708234	111708238	30008
37	3	6881	111751663	Reproduction	111708234	111708238	30008
38	3	6880	111730561	Reproduction	111708234	111708238	30244
39	3	6881	111751663	Reproduction	111708234	111708238	30244
40	3	6880	111730561	Meat_and_Carcass	111708234	111708238	152258
41	3	6881	111751663	Meat_and_Carcass	111708234	111708238	152258
42	3	6880	111730561	Meat_and_Carcass	111717521	111717525	225527
43	3	6881	111751663	Meat_and_Carcass	111717521	111717525	225527
44	4	7184	7873471	Milk	7839854	7839858	118382
45	4	7184	7873471	Milk	7851689	7851693	118383
46	4	7184	7873471	Milk	7878682	7878686	113676
47	4	7184	7873471	Milk	7878682	7878686	118072
48	4	7471	24239851	Meat_and_Carcass	24248260	24248264	228128
49	4	7471	24239851	Reproduction	24279008	24279012	212516
50	4	7471	24239851	Meat_and_Carcass	24288010	24288014	107768
51	4	7471	24239851	Meat_and_Carcass	24288010	24288014	107796
52	4	7515	26511448	Exterior	26511446	26511450	66124
53	4	7524	27021431	Production	27014514	27014518	65173
54	4	7910	50755462	Meat_and_Carcass	50781007	50781011	228112
55	4	8831	103979600	Health	103949847	103949851	96218
56	4	8831	103979600	Meat_and_Carcass	104013536	104013540	231600
57	4	8888	107787202	Reproduction	107749162	107749166	106782
58	6	11511	36184467	Milk	36141496	36141500	244115
59	6	11511	36184467	Milk	36141496	36141500	246435
60	6	11511	36184467	Milk	36141496	36141500	250700
61	6	11511	36184467	Production	36143407	36143411	190189
62	6	11511	36184467	Production	36143929	36143933	187391
63	6	11511	36184467	Production	36145023	36145027	183480
64	6	11511	36184467	Production	36145023	36145027	190272
65	6	11511	36184467	Production	36145254	36145258	182513
66	6	11511	36184467	Production	36145254	36145258	186692
67	6	11511	36184467	Production	36147416	36147420	187702
68	6	11511	36184467	Production	36147705	36147709	186912
69	6	11511	36184467	Production	36148373	36148377	182603
70	6	11511	36184467	Production	36148373	36148377	188134
71	6	11511	36184467	Production	36149039	36149043	186551
72	6	11511	36184467	Production	36150874	36150878	183765
73	6	11511	36184467	Production	36150874	36150878	186794
74	6	11511	36184467	Production	36151323	36151327	183528
75	6	11511	36184467	Production	36151323	36151327	187598
76	6	11511	36184467	Production	36153011	36153015	188352
77	6	11511	36184467	Production	36154453	36154457	188950
78	6	11511	36184467	Production	36156604	36156608	187155
79	6	11511	36184467	Production	36156892	36156896	183350
80	6	11511	36184467	Production	36156892	36156896	187196
81	6	11511	36184467	Production	36157620	36157624	23700
82	6	11511	36184467	Production	36157620	36157624	66352
83	6	11511	36184467	Production	36157620	36157624	66353
84	6	11511	36184467	Production	36157620	36157624	66354
85	6	11511	36184467	Production	36157620	36157624	66355
86	6	11511	36184467	Production	36157620	36157624	67222
87	6	11511	36184467	Production	36157620	36157624	67223
88	6	11511	36184467	Production	36157620	36157624	164068
89	6	11511	36184467	Production	36157620	36157624	164167
90	6	11511	36184467	Meat_and_Carcass	36157620	36157624	164343
91	6	11511	36184467	Production	36157620	36157624	164369
92	6	11511	36184467	Production	36157620	36157624	164488
93	6	11511	36184467	Production	36157620	36157624	183351
94	6	11511	36184467	Production	36157620	36157624	186777
95	6	11511	36184467	Production	36157986	36157990	187308
96	6	11511	36184467	Production	36158359	36158363	187062
97	6	11511	36184467	Production	36158498	36158502	186952
98	6	11511	36184467	Production	36159606	36159610	183352
99	6	11511	36184467	Production	36159606	36159610	187046
100	6	11511	36184467	Production	36160694	36160698	187096
101	6	11511	36184467	Production	36161273	36161277	187575
102	6	11511	36184467	Production	36161375	36161379	186498
103	6	11511	36184467	Production	36161625	36161629	187580
104	6	11511	36184467	Production	36161633	36161637	187885
105	6	11511	36184467	Production	36162950	36162954	187283
106	6	11511	36184467	Production	36163340	36163344	187023
107	6	11511	36184467	Production	36163428	36163432	186547
108	6	11511	36184467	Reproduction	36163939	36163943	14703
109	6	11511	36184467	Reproduction	36163939	36163943	14707
110	6	11511	36184467	Production	36163939	36163943	189833
111	6	11511	36184467	Production	36164020	36164024	189834
112	6	11511	36184467	Production	36164564	36164568	187124
113	6	11511	36184467	Production	36165001	36165005	186965
114	6	11511	36184467	Production	36165137	36165141	187629
115	6	11511	36184467	Production	36165150	36165154	188219
116	6	11511	36184467	Production	36165218	36165222	186478
117	6	11511	36184467	Production	36165305	36165309	187300
118	6	11511	36184467	Production	36165645	36165649	186485
119	6	11511	36184467	Production	36166108	36166112	187795
120	6	11511	36184467	Production	36166178	36166182	187257
121	6	11511	36184467	Production	36166205	36166209	187129
122	6	11511	36184467	Production	36166298	36166302	187368
123	6	11511	36184467	Reproduction	36166503	36166507	14704
124	6	11511	36184467	Milk	36166503	36166507	105821
125	6	11511	36184467	Production	36166503	36166507	189279
126	6	11511	36184467	Production	36166791	36166795	189278
127	6	11511	36184467	Production	36166795	36166799	182735
128	6	11511	36184467	Production	36166795	36166799	189277
129	6	11511	36184467	Production	36167325	36167329	183190
130	6	11511	36184467	Production	36167325	36167329	186569
131	6	11511	36184467	Production	36167828	36167832	183191
132	6	11511	36184467	Production	36167828	36167832	188015
133	6	11511	36184467	Production	36168124	36168128	183305
134	6	11511	36184467	Production	36168124	36168128	186440
135	6	11511	36184467	Production	36168592	36168596	182749
136	6	11511	36184467	Production	36168592	36168596	186494
137	6	11511	36184467	Production	36168720	36168724	182760
138	6	11511	36184467	Production	36168720	36168724	186560
139	6	11511	36184467	Production	36169712	36169716	187059
140	6	11511	36184467	Production	36169792	36169796	186511
141	6	11511	36184467	Milk	36170113	36170117	140218
142	6	11511	36184467	Production	36170133	36170137	186638
143	6	11511	36184467	Production	36170199	36170203	187467
144	6	11511	36184467	Production	36171684	36171688	188463
145	6	11511	36184467	Production	36172370	36172374	187237
146	6	11511	36184467	Milk	36172376	36172380	246431
147	6	11511	36184467	Production	36177199	36177203	183152
148	6	11511	36184467	Production	36177199	36177203	188011
149	6	11511	36184467	Production	36180643	36180647	187727
150	6	11511	36184467	Production	36181019	36181023	187465
151	6	11511	36184467	Production	36182792	36182796	187002
152	6	11511	36184467	Production	36183911	36183915	186509
153	6	11511	36184467	Milk	36185332	36185336	250783
154	6	11511	36184467	Milk	36186103	36186107	103888
155	6	11511	36184467	Milk	36186103	36186107	104752
156	6	11511	36184467	Production	36186581	36186585	188641
157	6	11511	36184467	Production	36186688	36186692	189503
158	6	11511	36184467	Production	36187039	36187043	188875
159	6	11511	36184467	Production	36189076	36189080	188768
160	6	11511	36184467	Production	36191254	36191258	187381
161	6	11511	36184467	Production	36198700	36198704	186480
162	6	11511	36184467	Production	36203596	36203600	187794
163	6	11511	36184467	Milk	36204034	36204038	138157
164	6	11511	36184467	Production	36204034	36204038	186572
165	6	11511	36184467	Milk	36205214	36205218	15002
166	6	11511	36184467	Production	36206851	36206855	188861
167	6	11511	36184467	Production	36210548	36210552	187623
168	6	11511	36184467	Production	36211595	36211599	187645
169	6	11511	36184467	Production	36212184	36212188	189171
170	6	11511	36184467	Production	36212912	36212916	187151
171	6	11511	36184467	Production	36216422	36216426	187483
172	6	11511	36184467	Milk	36218000	36218004	103890
173	6	11511	36184467	Milk	36218000	36218004	104754
174	6	11511	36184467	Milk	36219842	36219846	105620
175	6	11511	36184467	Production	36219842	36219846	186455
176	6	11511	36184467	Production	36221082	36221086	187299
177	6	11511	36184467	Production	36221438	36221442	187378
178	6	11511	36184467	Production	36225616	36225620	186759
179	6	11511	36184467	Production	36226441	36226445	186605
180	6	11511	36184467	Production	36226963	36226967	23701
181	6	11511	36184467	Production	36226963	36226967	67245
182	6	11511	36184467	Production	36226963	36226967	67246
183	6	11511	36184467	Production	36226963	36226967	67247
184	6	11511	36184467	Milk	36226963	36226967	105509
185	6	11511	36184467	Production	36226963	36226967	164069
186	6	11511	36184467	Production	36226963	36226967	164370
187	6	11511	36184467	Production	36226963	36226967	186841
188	6	11511	36184467	Production	36227839	36227843	182676
189	6	11511	36184467	Production	36227839	36227843	188422
190	6	11511	36184467	Production	36232422	36232426	187967
191	6	11511	36184467	Production	36232742	36232746	187367
192	6	11511	36184467	Production	36233671	36233675	188839
193	14	25167	15929822	Milk	15895119	15895123	25606
194	14	25167	15929822	Milk	15895119	15895123	26355
195	14	25167	15929822	Meat_and_Carcass	15895119	15895123	36989
196	14	25167	15929822	Milk	15904094	15904098	104606
197	14	25167	15929822	Milk	15905538	15905542	104607
198	14	25167	15929822	Exterior	15929820	15929824	220989
199	14	25167	15929822	Production	15964283	15964287	122755
200	14	25167	15929822	Production	15964283	15964287	123539
201	14	25167	15929822	Production	15965017	15965021	122756
202	14	25167	15929822	Production	15965017	15965021	123540
203	14	25167	15929822	Production	15967515	15967519	122757
204	14	25167	15929822	Production	15967515	15967519	123541
205	14	25167	15929822	Production	15969384	15969388	122758
206	14	25167	15929822	Production	15969384	15969388	123542
207	14	25167	15929822	Production	15972216	15972220	122759
208	14	25167	15929822	Production	15972216	15972220	123543
209	14	25167	15929822	Production	15977822	15977826	122760
210	14	25167	15929822	Production	15977822	15977826	123544
211	15	26782	33066384	Production	33066382	33066386	68791
212	15	26782	33066384	Meat_and_Carcass	33107568	33107572	228743
213	15	27199	57930281	Milk	57892792	57892796	242339
214	15	27199	57930281	Milk	57914040	57914044	248429
215	15	27199	57930281	Milk	57926776	57926780	242396
216	15	27199	57930281	Milk	57926776	57926780	248428
217	15	27199	57930281	Milk	57949776	57949780	242391
218	17	29742	46487068	Milk	46449873	46449877	162016
219	17	29742	46487068	Milk	46449873	46449877	163641
220	17	29742	46487068	Milk	46452018	46452022	162017
221	17	29742	46487068	Milk	46452018	46452022	163642
222	17	29742	46487068	Milk	46455275	46455279	163230
223	17	29742	46487068	Milk	46455275	46455279	163643
224	17	29742	46487068	Milk	46457135	46457139	163231
225	17	29742	46487068	Milk	46457135	46457139	163644
226	17	29742	46487068	Milk	46460742	46460746	163159
227	17	29742	46487068	Milk	46460742	46460746	163583
228	17	29742	46487068	Milk	46463937	46463941	163232
229	17	29742	46487068	Milk	46463937	46463941	163645
230	17	29742	46487068	Milk	46474328	46474332	162015
231	17	29742	46487068	Milk	46474328	46474332	163640
232	17	29742	46487068	Milk	46474997	46475001	162014
233	17	29742	46487068	Milk	46474997	46475001	163639
234	17	29742	46487068	Milk	46476243	46476247	162013
235	17	29742	46487068	Milk	46476243	46476247	163638
236	17	29742	46487068	Milk	46476816	46476820	162012
237	17	29742	46487068	Milk	46476816	46476820	163637
238	17	29742	46487068	Milk	46480499	46480503	161948
239	17	29742	46487068	Milk	46480499	46480503	162037
240	17	29742	46487068	Milk	46481110	46481114	161941
241	17	29742	46487068	Milk	46481110	46481114	162030
242	17	29742	46487068	Milk	46481709	46481713	162019
243	17	29742	46487068	Milk	46481709	46481713	162100
244	17	29742	46487068	Milk	46482674	46482678	162020
245	17	29742	46487068	Milk	46482674	46482678	162101
246	17	29742	46487068	Milk	46483484	46483488	162021
247	17	29742	46487068	Milk	46483484	46483488	162102
248	17	29742	46487068	Milk	46529284	46529288	163162
249	17	29742	46487068	Milk	46529284	46529288	163584
250	17	29772	47884497	Milk	47837023	47837027	162764
251	17	29772	47884497	Milk	47844960	47844964	162876
252	17	29772	47884497	Milk	47851806	47851810	162921
253	17	29772	47884497	Milk	47855252	47855256	162648
254	17	29772	47884497	Production	47856400	47856404	23869
255	17	29772	47884497	Production	47856400	47856404	68904
256	17	29772	47884497	Exterior	47856400	47856404	102064
257	17	29772	47884497	Exterior	47884495	47884499	102065
258	17	29772	47884497	Milk	47924476	47924480	163030
259	17	29772	47884497	Milk	47924476	47924480	163517
260	17	29772	47884497	Milk	47926919	47926923	162858
261	17	29772	47884497	Milk	47926919	47926923	163422
262	17	29784	48800954	Milk	48759668	48759672	63966
263	20	33665	65351653	Milk	65321359	65321363	176120
264	20	33665	65351653	Milk	65350145	65350149	69962
265	20	33665	65351653	Milk	65352215	65352219	69963
266	20	33665	65351653	Milk	65357974	65357978	69964
267	20	33665	65351653	Milk	65363596	65363600	69626
268	20	33665	65351653	Health	65367232	65367236	167647
269	21	33836	4055731	Reproduction	4074797	4074801	145524
270	21	33836	4055731	Reproduction	4079204	4079208	145458
271	21	33836	4055731	Reproduction	4079247	4079251	145445
272	21	33836	4055731	Reproduction	4085667	4085671	145389
273	21	33836	4055731	Reproduction	4085667	4085671	146710
274	21	33836	4055731	Reproduction	4085817	4085821	146703
275	23	36688	44822126	Milk	44787659	44787663	254748
276	23	36688	44822126	Milk	44789048	44789052	254706
277	23	36688	44822126	Meat_and_Carcass	44807487	44807491	226965
278	23	36688	44822126	Meat_and_Carcass	44807487	44807491	229458
279	23	36688	44822126	Meat_and_Carcass	44807487	44807491	234754
280	23	36688	44822126	Milk	44808987	44808991	254767
281	23	36688	44822126	Milk	44830780	44830784	254833
282	23	36688	44822126	Milk	44834127	44834131	254725
283	23	36688	44822126	Milk	44845256	44845260	254688
284	23	36688	44822126	Milk	44848146	44848150	254689
285	23	36688	44822126	Milk	44859653	44859657	254769
286	24	36876	857728	Exterior	857726	857730	66121
287	24	36876	857728	Reproduction	885598	885602	106889
288	28	40795	20016672	Milk	20016670	20016674	112226
289	28	40795	20016672	Milk	20016670	20016674	118869
290	28	40795	20016672	Milk	20017763	20017767	112227
291	28	40795	20016672	Milk	20017763	20017767	118870
292	28	40795	20016672	Milk	20045011	20045015	118664
293	28	40795	20016672	Milk	20060184	20060188	118665
294	28	41057	35807366	Reproduction	35807364	35807368	138601
295	28	41057	35807366	Reproduction	35807364	35807368	138602
296	29	41785	33039863	Production	33039861	33039865	69450
297	29	41785	33039863	Production	33039861	33039865	69451

QTL type

#plotting the percentage of each QTL class annoatted
oldpar <- par(mar=c(1,15,0.5,1))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1.5)

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Production QTLs

#Setting margin parameter to better fit the axis labels
oldpar<-par(mar=c(5,20,1,1))
#plotting the percentage of each trait annoatted as a Reproduction QTL
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Production")

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Reproduction QTLs

#Setting margin parameter to better fit the axis labels
oldpar<-par(mar=c(5,20,1,1))
#plotting the percentage of each trait annoatted as a Reproduction QTL
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

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Milk QTLs

#Setting margin parameter to better fit the axis labels
oldpar<-par(mar=c(5,20,1,1))
#plotting the percentage of each trait annoatted as a Reproduction QTL
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Milk")

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Meat and Carcass QTLs

#Setting margin parameter to better fit the axis labels
oldpar<-par(mar=c(5,20,1,1))
#plotting the percentage of each trait annoatted as a Reproduction QTL
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class=""Meat_and_Carcass"")

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Health QTLs

#Setting margin parameter to better fit the axis labels
oldpar<-par(mar=c(5,20,1,1))
#plotting the percentage of each trait annoatted as a Reproduction QTL
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

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Exterior QTLs

#Setting margin parameter to better fit the axis labels
oldpar<-par(mar=c(5,20,1,1))
#plotting the percentage of each trait annoatted as a Reproduction QTL
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Exterior")

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24.6.1 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_enrich_qtl_genome_name.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_enrich_qtl_genome_type.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
27	Metabolic body weight	83	4039	292	163224	0.0000000	0.0000000	Production
29	Milk butyric acid content	22	828	292	163224	0.0000000	0.0000000	Milk
30	Milk caproic acid content	18	675	292	163224	0.0000000	0.0000000	Milk
12	Dry matter intake	17	2186	292	163224	0.0000006	0.0000090	Production
2	Aggressive behavior	2	8	292	163224	0.0000887	0.0008719	Exterior
25	Length of productive life	13	2004	292	163224	0.0000815	0.0008719	Production
35	Milk pentadecylic acid content	5	280	292	163224	0.0001640	0.0013823	Milk
33	Milk iron content	4	217	292	163224	0.0006685	0.0049302	Milk
15	Dystocia	2	31	292	163224	0.0014331	0.0090411	Reproduction
16	Fecal larva count	3	125	292	163224	0.0015324	0.0090411	Health
4	Body weight	16	4289	292	163224	0.0050041	0.0268400	Production
14	Duration of inactivity during open field test	1	9	292	163224	0.0159862	0.0785990	Exterior
13	Duration of exploration during novel object test	1	16	292	163224	0.0282437	0.1281828	Exterior
5	Body weight gain	6	1354	292	163224	0.0362258	0.1526660	Production
3	Anti-Müllerian hormone level	1	26	292	163224	0.0454908	0.1789305	Health
46	Polyunsaturated fatty acid content	1	28	292	163224	0.0489034	0.1803314	Meat and Carcass
45	Palmitoleic acid content	1	46	292	163224	0.0790752	0.2744374	Meat and Carcass
42	Muscle sodium content	1	56	292	163224	0.0954231	0.3127758	Meat and Carcass
28	Milk alpha-S1-casein percentage	1	65	292	163224	0.1098888	0.3382449	Milk
43	Myristoleic acid content	1	68	292	163224	0.1146593	0.3382449	Meat and Carcass
39	Milk unglycosylated kappa-casein percentage	7	2351	292	163224	0.1315212	0.3695119	Milk
21	Interdigital hyperplasia	1	93	292	163224	0.1534352	0.4052354	Exterior
41	Multiple birth	1	96	292	163224	0.1579731	0.4052354	Reproduction
19	Gestation length	2	636	292	163224	0.3149170	0.7146195	Reproduction
23	Interval to first estrus after calving	3	1053	292	163224	0.2917668	0.7146195	Reproduction
36	Milk protein percentage	18	8803	292	163224	0.3139794	0.7146195	Milk
8	Calving ease	6	3819	292	163224	0.6804471	0.9778366	Reproduction
10	Conception rate	2	1255	292	163224	0.6577041	0.9778366	Reproduction
17	Feet and leg conformation	1	627	292	163224	0.6752950	0.9778366	Exterior
18	Foot angle	1	672	292	163224	0.7005282	0.9778366	Exterior
22	Interval from first to last insemination	1	445	292	163224	0.5497190	0.9778366	Reproduction
24	Lean meat yield	1	621	292	163224	0.6717742	0.9778366	Meat and Carcass
38	Milk riboflavin content	1	509	292	163224	0.5986072	0.9778366	Milk
48	PTA type	1	627	292	163224	0.6752950	0.9778366	Production
49	Rear leg placement - side view	1	430	292	163224	0.5374280	0.9778366	Exterior
50	Rump width	1	526	292	163224	0.6106790	0.9778366	Production
52	Somatic cell score	2	1122	292	163224	0.5971200	0.9778366	Health
53	Stillbirth	2	1363	292	163224	0.7013705	0.9778366	Reproduction
54	Strength	1	664	292	163224	0.6961897	0.9778366	Exterior
55	Subcutaneous fat thickness	1	331	292	163224	0.4474847	0.9778366	Meat and Carcass
57	Udder attachment	1	655	292	163224	0.6912339	0.9778366	Exterior
58	Udder depth	1	695	292	163224	0.7126605	0.9778366	Exterior
59	Udder height	1	504	292	163224	0.5949862	0.9778366	Exterior
6	Bovine respiratory disease susceptibility	1	789	292	163224	0.7573589	0.9935536	Health
20	Inseminations per conception	1	790	292	163224	0.7577951	0.9935536	Reproduction
1	Age at puberty	1	8222	292	163224	0.9999997	0.9999997	Reproduction
7	Bovine tuberculosis susceptibility	1	1155	292	163224	0.8744991	0.9999997	Health
9	Carcass weight	1	2020	292	163224	0.9737344	0.9999997	Meat and Carcass
11	Connective tissue amount	1	3142	292	163224	0.9965892	0.9999997	Meat and Carcass
26	Marbling score	1	1817	292	163224	0.9620577	0.9999997	Meat and Carcass
31	Milk fat percentage	9	10941	292	163224	0.9979006	0.9999997	Milk
32	Milk fat yield	2	8220	292	163224	0.9999954	0.9999997	Milk
34	Milk kappa-casein percentage	5	4499	292	163224	0.9064431	0.9999997	Milk
37	Milk protein yield	1	3093	292	163224	0.9962701	0.9999997	Milk
40	Milk yield	4	6432	292	163224	0.9970834	0.9999997	Milk
44	Net merit	1	903	292	163224	0.8023701	0.9999997	Production
47	Pregnancy rate	1	944	292	163224	0.8164356	0.9999997	Reproduction
51	Shear force	2	2954	292	163224	0.9692776	0.9999997	Meat and Carcass
56	Tenderness score	4	3483	292	163224	0.8712212	0.9999997	Meat and Carcass

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Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	12	9077	292	163224	0.8914062	1
2	Health	8	5889	292	163224	0.8293292	1
3	Meat and Carcass	15	18258	292	163224	0.9998999	1
4	Milk	97	75352	292	163224	0.9999974	1
5	Production	138	19640	292	163224	0.0000000	0
6	Reproduction	22	35008	292	163224	1.0000000	1

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24.7 BLUPF90+ GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend My Image

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24.8 BLUPF90+ VEP

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rs110079750 My Image

rs109276211 My Image

rs109631116 My Image

rs42029843

rs110428837 My Image

rs110081798 My Image

rs110160157 My Image

rs110038841 My Image

rs109868537 My Image

rs110565206 My Image

rs42751504 My Image

rs110121846 My Image

rs110935391 My Image

rs109584097 My Image

rs41585925 My Image

rs110506037 My Image

rs109273103 My Image

rs109575643 My Image

rs43305418 My Image

rs43377276 My Image

rs43691687 My Image

rs41764450 My Image

rs42305073 No

rs42462935 No

rs42752353 My Image

rs81154019 No

rs41645754 My Image

rs41566051 My Image

rs41636137 My Image

rs41615935 My Image

rs29026778 My Image

rs29013980 My Image

rs29023486 My Image

rs29027498 My Image

rs29017061 My Image

rs29018741 My Image

rs29013301 My Image

rs29012492 My Image

It is interesting that 3 significant SNPs falled in the same gene CSMD2 on chromosome 3 rs109868537 rs42751504 rs42752353

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For the gene CNTNAP2 on chromosome 4 also have 2 significant SNPs rs41566051 rs29012492

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This code will generate several files, among them:

chrsnp_pval: which has the -log10(pvalue) the 3rd column
Pft1e3.R: a r-code to generate the Manhattan plot in R

24.8.0.1 Manhattan Plots for BLUPF90 GWAS

24.8.0.1.1 Genome Independent Segment

To make the Manhattan Plot considering Genome Independent Segment we should run the code bellow. This code has part of the code in the file Pft1e3.R

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)


setwd("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO")
# Read in and process data for Manhattan plot
yyy1 <- read.table("chrsnp_pval")
yyy <- yyy1[order(yyy1$V4), ]
zzz <- yyy[which(yyy$V1 == 1 & yyy$V2 == 3), ]
n <- nrow(zzz)
y <- zzz[, 4]
x <- zzz[, 3]
chr1 <- zzz[, 5]
chr <- NULL
pos <- NULL

for (i in unique(yyy$V5)) {
  zz <- yyy[yyy$V5 == i, ]
  key <- zz$V4
  medio <- round(nrow(zz) / 2, 0)
  z <- key[medio]
  pos <- c(pos, z)
}

chrn <- unique(yyy$V5)
one <- which(chr1 %% 4 == 0)
two <- which(chr1 %% 4 == 1)
three <- which(chr1 %% 4 == 2)
four <- which(chr1 %% 4 == 3)
chr[one] <- "darkgoldenrod"
chr[two] <- "darkorchid"
chr[three] <- "blue"
chr[four] <- "forestgreen"

# Create Manhattan plot with Bonferroni line and legend
pdf(file = "Pft1e3_manplot_with_bonf_ind_seg.pdf", family = "sans", height = 27.8, width = 50, pointsize = 20, bg = "white")
par(mfrow = c(1, 1), family = "sans", cex = 1.5, font = 2)
plot(y, x, xaxt = "n", main = "Manhattan Plot SNP p_value - Trait: 1 Effect: 3", xlab = "", ylab = "-log10(p-value)", pch = 20, xlim = c(1, n), ylim = c(0, max(x)), col = chr)

# Add Bonferroni line
abline(h = bonf, col = "red", lwd = 2, lty = 2)

# Add legend for Bonferroni line
legend("topright", legend = "Bonferroni correction for genome independent segments", col = "red", lwd = 2, lty = 2, cex = 1)

axis(1, at = pos, labels = chrn)
dev.off()

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For additional analysis like Variant Effect Prediction (VEP) we need the rsID, to get the rsID we use the software SNPChimp which requires SNP_names, but the BLUPF90 output have only the Chromosome and Position of the SNPs, so we are going to perfome these two steps to get one file with t he significant SNPs + SNP_name + rsID

24.8.1 Results

We have 2 different output files

Variance components: blupf90.log

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In this file we can find the heritabilit (SD) and for instance the convergence (similarity)

Solutions: solutions.orig
In this file we will find the solutions (results) for each effect

In our example:

EFFECT 1: Birth Year, has 4 levels (2018, 2019, 2020 and 2021), and the solution that for this fixed effect is how much each level add.
EFFECT 2: Sampling date, has 23 levels, and the solutions
EFFECT 3: Animal random effect, has one for each animal and it is the EBV or GEBV.

24.8.2 Variance components estimation with the whole sample

We’LL estimate the GRM on GCTA fro all 252 animals.

First I will need to build a

24.8.3 GWAS

Now we’ll estimate the Variance Components for all 252 animals on BLUPF90+ With the Variance Components Estimations we’ll perform a GWAS on BLUPF90+ only for the animals with Extreme Phenotypes.

24.8.4 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result_NO_CONSTRAIN.loco.mlma", 
                  head=T, stringsAsFactors = F)


gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf<- -log10(0.15/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topright", col="red2", lwd=2, c("Bonferroni corr. for ind. segments"), bty="n")


library(tidyverse)

SNP_sig_bonf_ind_seg <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf_ind_seg, file = "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/SNP_sig15_Bonf_ind_seg.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(SNP_sig_bonf_ind_seg, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/SNP_sig15_Bonf_ind_seg.txt")

The SNPs below are the significant after the correction above

X	Chr	SNP	bp	A1	A2	Freq	b	se	p	logP
1	3	BTB-00148619	105840426	A	C	0.321429	410.739	104.530	0.0000852	4.069736
2	3	ARS-BFGL-NGS-118207	110866523	A	C	0.421429	402.730	103.634	0.0001019	3.991928
3	22	ARS-BFGL-NGS-23411	5134078	A	C	0.214286	415.637	109.100	0.0001391	3.856526

My Image

Bellow we inserted the rsID searched at SNPCHIMP

X	Chr	rsID	SNP	bp	A1	A2	Freq	b	se	p	logP
1	3	rs43357861	BTB-00148619	105840426	A	C	0.321429	410.739	104.530	0.0000852	4.069736
2	3	rs110081798	ARS-BFGL-NGS-118207	110866523	A	C	0.421429	402.730	103.634	0.0001019	3.991928
3	22	rs110031217	ARS-BFGL-NGS-23411	5134078	A	C	0.214286	415.637	109.100	0.0001391	3.856526

24.8.5 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.csv(file = "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/SNP_sig15_Bonf_ind_seg.csv")

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_genes_extr_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_genes_extr_15.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_qtl_extr_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_qtl_extr_15.csv")

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	3	BTB-00148619	105840426	3	105807211	105810027	2817	+	ENSBTAG00000020622	COL9A2	lncRNA
2	3	BTB-00148619	105840426	3	105824376	105867157	42782	-	ENSBTAG00000044178	ZMPSTE24	protein_coding
3	3	BTB-00148619	105840426	3	105884724	105887360	2637	-	ENSBTAG00000019698	TMCO2	protein_coding
4	3	ARS-BFGL-NGS-118207	110866523	3	110769753	110827938	58186	+	ENSBTAG00000013867	DLGAP3	protein_coding
5	3	ARS-BFGL-NGS-118207	110866523	3	110835276	110839536	4261	+	ENSBTAG00000014235	SMIM12	protein_coding
6	3	ARS-BFGL-NGS-118207	110866523	3	110888198	110890904	2707	-	ENSBTAG00000013881	GJA4	protein_coding
7	3	ARS-BFGL-NGS-118207	110866523	3	110900183	110905849	5667	-	ENSBTAG00000012584	GJB3	protein_coding
8	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	3	BTB-00148619	105840426	Production	105835011	105835015	287197
2	3	ARS-BFGL-NGS-118207	110866523	Meat_and_Carcass	110866521	110866525	152332
3	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
4	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
5	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601

24.8.5.1 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_enrich_qtl_genome_name_extr_15.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_enrich_qtl_genome_type_extr_15.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
2	Muscle carnosine content	3	67	5	163224	0.0000000	0.0000000	Meat and Carcass
3	Polyunsaturated fatty acid content	1	28	5	163224	0.0008574	0.0012861	Meat and Carcass
1	Body depth	1	616	5	163224	0.0187281	0.0187281	Production

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Meat and Carcass	4	18258	5	163224	0.0007125	0.0014251
2	Production	1	19640	5	163224	0.4732476	0.4732476

My Image

24.8.6 gPROFILER online

From the online version of GPROFILER i got the following results.

Legend My Image

My Image

25 GWAS on GCTA

pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv", header = T)
ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)


# Create data_final$cdn_id by matching IDs with elora_id
data_final$cdn_id <- ids_eq$cdn_id[match(data_final$ID, ids_eq$elora_id)]
fixeff <- data_final[,c("ID", "BIRTH_YEAR", "Sampling_date", "cdn_id")]
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id, ]
fixeff$FID <- "HO"
fixeff <- fixeff[, c("FID", "cdn_id", "BIRTH_YEAR", "Sampling_date")]

identical(fixeff$cdn_id, pheno$cdn_id)

write.table(fixeff, "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/fixeff.txt", quote = F, row.names = F, col.names = T)

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

25.1 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

{r eval=FALSE}
Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)

gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf<- -log10(0.05/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topleft", col="red2", lwd=2, c("Bonferroni corr. for ind. segments"), bty="n")


library(tidyverse)

SNP_sig_bonf_ind_seg <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf_ind_seg, file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/SNP_sig_Bonf_ind_seg.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(SNP_sig_bonf_ind_seg, "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/SNP_sig_Bonf_ind_seg2.csv")

The SNPs below are the significant after the correction above

X	Chr	SNP	bp	A1	A2	Freq	b	se	p	logP
1	22	ARS-BFGL-NGS-23411	5134078	A	C	0.1872510	196.185	43.9934	8.20e-06	5.085250
2	22	ARS-BFGL-NGS-26419	46052082	A	C	0.0816733	295.245	66.9504	1.03e-05	4.985404
3	26	BTB-00926636	17857480	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
4	26	BTA-62125-no-rs	17899619	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
5	28	BTA-99379-no-rs	41666186	A	C	0.0498008	394.376	83.4532	2.30e-06	5.639587

My Image

Bellow we inserted the rsID searched at SNPCHIMP

X	Chr	rsID	SNP	bp	A1	A2	Freq	b	se	p	logP
1	22	rs110031217	ARS-BFGL-NGS-23411	5134078	A	C	0.1872510	196.185	43.9934	8.20e-06	5.085250
2	22	rs110991998	ARS-BFGL-NGS-26419	46052082	A	C	0.0816733	295.245	66.9504	1.03e-05	4.985404
3	26	rs42089058	BTB-00926636	17857480	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
4	26	rs41644634	BTA-62125-no-rs	17899619	C	A	0.0258964	491.154	112.4840	1.26e-05	4.898635
5	28	rs41567074	BTA-99379-no-rs	41666186	A	C	0.0498008	394.376	83.4532	2.30e-06	5.639587

25.1.1 Option 2: Manhattan Plot

gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)
# Prepare the dataset
don <- gwas %>% 
  group_by(Chr) %>% 
  summarise(chr_len = max(bp)) %>% 
  mutate(tot = cumsum(as.numeric(chr_len)) - as.numeric(chr_len)) %>%
  select(-chr_len) %>%
  left_join(gwas, ., by = c("Chr" = "Chr")) %>%
  arrange(Chr, bp) %>%
  mutate(BPcum = as.numeric(bp) + tot) %>%
  mutate(is_highlight = ifelse(logP > bonf, "yes", "no")) %>%
  mutate(is_annotate = ifelse(logP > bonf, "yes", "no"))

# Remove rows with missing BPcum values
don <- don %>% filter(!is.na(BPcum))

# Prepare X axis
axisdf <- don %>% 
  group_by(Chr) %>% 
  summarize(center = (max(BPcum) + min(BPcum)) / 2)

# Find the maximum logP value for setting y-axis limits
max_logP <- max(don$logP, na.rm = TRUE)

# Make the plot
ggplot(don, aes(x = BPcum, y = logP)) +
  geom_point(aes(color = as.factor(Chr)), alpha = 0.8, size = 1.3) +
  scale_color_manual(values = rep(c("grey", "skyblue"), 22)) +
  scale_x_continuous(label = axisdf$Chr, breaks = axisdf$center) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, max_logP + 1)) + # Set y-axis limit
  geom_point(data = subset(don, is_highlight == "yes"), color = "orange", size = 2) +
  geom_text_repel(data = subset(don, is_annotate == "yes"), aes(label = SNP), size = 2) +
  theme_bw() +
  theme(
    legend.position = "none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  labs(title = "GWAS Cortisol", x = "Chromosomes", y = "-log10(p)")

26 VEP

After insert all rsID on VEP the summary is shown below:

As can be seen, the VEP only analysed 14 variants

Bellow I’m gonna show the “genome view” for each SNP (variant) recovered from VEP:

rs110031217 My Image

rs110991998 My Image

rs42089058 My Image

rs41644634 My Image

rs41567074 My Image

ps. the unique reference genome available in the VEP is the ARS-UCD1.3, which is not a problem once it is working with the rsID, which for sure is our variant.

27 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.csv(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/SNP_sig_Bonf_ind_seg.csv")

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_genes2.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_genes2.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_qtl2.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_qtl_clean2.csv")

Dowloading the .gtf file from Ensembl https://useast.ensembl.org/info/data/ftp/index.html

Downloading the .gff file from AnimalQTLdb https://www.animalgenome.org/cgi-bin/QTLdb/index

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	22	ARS-BFGL-NGS-26419	46052082	22	45924535	46818511	893977	-	ENSBTAG00000013117	CACNA2D3	protein_coding
2	22	ARS-BFGL-NGS-26419	46052082	22	46051998	46062657	10660	+	ENSBTAG00000013124	LRTM1	protein_coding
3	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding
4	26	BTB-00926636	17857480	26	17703169	17846407	143239	-	ENSBTAG00000011743	TLL2	protein_coding
5	26	BTB-00926636	17857480	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
6	26	BTA-62125-no-rs	17899619	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
7	26	BTA-62125-no-rs	17899619	26	17925643	18029878	104236	-	ENSBTAG00000019872	PIK3AP1	protein_coding
8	28	BTA-99379-no-rs	41666186	28	41609015	41649016	40002	-	ENSBTAG00000007540	GLUD1	protein_coding
9	28	BTA-99379-no-rs	41666186	28	41650333	41763371	113039	+	ENSBTAG00000019194	SHLD2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
2	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
3	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601
4	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51802
5	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51803
6	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51804
7	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51805
8	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51806
9	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51807
10	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51808
11	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51809
12	22	ARS-BFGL-NGS-26419	46052082	Health	46052080	46052084	51810
13	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51811
14	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51812
15	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51813
16	22	ARS-BFGL-NGS-26419	46052082	Milk	46073517	46073521	243365
17	26	BTB-00926636	17857480	Milk	17809768	17809772	195264
18	26	BTB-00926636	17857480	Milk	17809768	17809772	200269
19	26	BTB-00926636	17857480	Milk	17809768	17809772	200270
20	26	BTB-00926636	17857480	Milk	17809768	17809772	205295
21	26	BTB-00926636	17857480	Milk	17810363	17810367	198859
22	26	BTB-00926636	17857480	Milk	17810363	17810367	203868
23	26	BTB-00926636	17857480	Milk	17810363	17810367	203869
24	26	BTB-00926636	17857480	Milk	17810363	17810367	210297
25	26	BTB-00926636	17857480	Milk	17811021	17811025	198858
26	26	BTB-00926636	17857480	Milk	17811021	17811025	203866
27	26	BTB-00926636	17857480	Milk	17811021	17811025	203867
28	26	BTB-00926636	17857480	Milk	17811021	17811025	210296
29	26	BTB-00926636	17857480	Milk	17811725	17811729	197493
30	26	BTB-00926636	17857480	Milk	17811725	17811729	202704
31	26	BTB-00926636	17857480	Milk	17812273	17812277	195685
32	26	BTB-00926636	17857480	Milk	17812273	17812277	200785
33	26	BTB-00926636	17857480	Milk	17813212	17813216	195285
34	26	BTB-00926636	17857480	Milk	17813212	17813216	200301
35	26	BTB-00926636	17857480	Milk	17813978	17813982	196236
36	26	BTB-00926636	17857480	Milk	17813978	17813982	201468
37	26	BTB-00926636	17857480	Milk	17815237	17815241	198002
38	26	BTB-00926636	17857480	Milk	17815237	17815241	203190
39	26	BTB-00926636	17857480	Milk	17817223	17817227	34750
40	26	BTB-00926636	17857480	Milk	17817223	17817227	195586
41	26	BTB-00926636	17857480	Milk	17821297	17821301	197235
42	26	BTB-00926636	17857480	Milk	17821297	17821301	202455
43	26	BTB-00926636	17857480	Milk	17822209	17822213	197783
44	26	BTB-00926636	17857480	Milk	17822209	17822213	202983
45	26	BTB-00926636	17857480	Milk	17823105	17823109	196204
46	26	BTB-00926636	17857480	Milk	17823105	17823109	201419
47	26	BTB-00926636	17857480	Milk	17824770	17824774	196867
48	26	BTB-00926636	17857480	Milk	17824770	17824774	202101
49	26	BTB-00926636	17857480	Milk	17833551	17833555	195748
50	26	BTB-00926636	17857480	Milk	17833551	17833555	200855
51	26	BTB-00926636	17857480	Milk	17833551	17833555	206213
52	26	BTB-00926636	17857480	Milk	17865069	17865073	197144
53	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	197144
54	26	BTB-00926636	17857480	Milk	17865069	17865073	202367
55	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	202367
56	26	BTB-00926636	17857480	Milk	17865069	17865073	208485
57	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	208485
58	26	BTB-00926636	17857480	Milk	17871201	17871205	197611
59	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	197611
60	26	BTB-00926636	17857480	Milk	17871201	17871205	202816
61	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	202816
62	26	BTB-00926636	17857480	Milk	17871201	17871205	209076
63	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	209076
64	26	BTB-00926636	17857480	Milk	17885871	17885875	63656
65	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	63656
66	26	BTB-00926636	17857480	Milk	17885871	17885875	199068
67	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	199068
68	26	BTB-00926636	17857480	Milk	17885871	17885875	204113
69	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204113
70	26	BTB-00926636	17857480	Milk	17885871	17885875	204114
71	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204114
72	26	BTB-00926636	17857480	Milk	17885871	17885875	210399
73	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	210399
74	26	BTB-00926636	17857480	Milk	17895273	17895277	63657
75	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	63657
76	26	BTB-00926636	17857480	Milk	17895273	17895277	199069
77	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	199069
78	26	BTB-00926636	17857480	Milk	17895273	17895277	204115
79	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204115
80	26	BTB-00926636	17857480	Milk	17895273	17895277	204116
81	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204116
82	26	BTB-00926636	17857480	Milk	17895273	17895277	210400
83	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	210400
84	26	BTB-00926636	17857480	Milk	17895803	17895807	199070
85	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	199070
86	26	BTB-00926636	17857480	Milk	17895803	17895807	204117
87	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204117
88	26	BTB-00926636	17857480	Milk	17895803	17895807	204118
89	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204118
90	26	BTB-00926636	17857480	Milk	17895803	17895807	210401
91	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	210401
92	26	BTB-00926636	17857480	Milk	17900304	17900308	199071
93	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	199071
94	26	BTB-00926636	17857480	Milk	17900304	17900308	204119
95	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204119
96	26	BTB-00926636	17857480	Milk	17900304	17900308	204120
97	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204120
98	26	BTB-00926636	17857480	Milk	17900304	17900308	210402
99	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	210402
100	26	BTB-00926636	17857480	Milk	17902932	17902936	63658
101	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	63658
102	26	BTB-00926636	17857480	Milk	17902932	17902936	199072
103	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	199072
104	26	BTB-00926636	17857480	Milk	17902932	17902936	204121
105	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204121
106	26	BTB-00926636	17857480	Milk	17902932	17902936	204122
107	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204122
108	26	BTB-00926636	17857480	Milk	17902932	17902936	210403
109	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	210403
110	26	BTB-00926636	17857480	Milk	17906861	17906865	63660
111	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	63660
112	26	BTB-00926636	17857480	Milk	17906861	17906865	199074
113	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	199074
114	26	BTB-00926636	17857480	Milk	17906861	17906865	204125
115	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204125
116	26	BTB-00926636	17857480	Milk	17906861	17906865	204126
117	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204126
118	26	BTB-00926636	17857480	Milk	17906861	17906865	210405
119	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	210405
120	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	63654
121	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	199076
122	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204129
123	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204130
124	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	210407
125	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	63655
126	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	197826
127	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203026
128	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203027
129	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	209354
130	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	195860
131	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201008
132	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201009
133	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	206462
134	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	33984
135	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195668
136	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195669
137	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195670
138	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	200758
139	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	199092
140	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	204151
141	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107048
142	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107227

27.1 QTL annotation on GALLO

QTL type

#GALLO
par(mar=c(8,20,8,8))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1)

My Image

QTL type

#GALLO
par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

Reproduction My Image

Health My Image

27.2 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_enrich_qtl_genome_name_2.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GALLO_BY_SD/out_enrich_qtl_genome_type_2.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
5	Milk C14 index	35	4437	108	163224	0.0000000	0.0000000	Milk
9	Milk myristoleic acid content	26	3047	108	163224	0.0000000	0.0000000	Milk
10	Milk palmitoleic acid content	15	2422	108	163224	0.0000000	0.0000000	Milk
6	Milk C16 index	12	2002	108	163224	0.0000000	0.0000001	Milk
12	Muscle carnosine content	3	67	108	163224	0.0000131	0.0000497	Meat and Carcass
14	Pregnancy rate	2	944	108	163224	0.1296627	0.4105985	Reproduction
18	Teat length	1	300	108	163224	0.1802436	0.4892327	Exterior
15	Rear leg placement - side view	1	430	108	163224	0.2479752	0.5235032	Exterior
17	Stillbirth	2	1363	108	163224	0.2280294	0.5235032	Reproduction
3	Foot angle	1	672	108	163224	0.3596272	0.6162877	Exterior
7	Milk capric acid content	1	912	108	163224	0.4541067	0.6162877	Milk
8	Milk myristic acid content	1	902	108	163224	0.4504612	0.6162877	Milk
13	Net merit	1	903	108	163224	0.4508269	0.6162877	Production
19	Udder depth	1	695	108	163224	0.3693423	0.6162877	Exterior
16	Somatic cell score	1	1122	108	163224	0.5253603	0.6654564	Health
2	Conception rate	1	1255	108	163224	0.5656375	0.6716945	Reproduction
1	Calving ease	2	3819	108	163224	0.7219243	0.7776744	Reproduction
4	Length of productive life	1	2004	108	163224	0.7367441	0.7776744	Production
11	Milk yield	1	6432	108	163224	0.9870080	0.9870080	Milk

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	4	9077	108	163224	0.8569775	0.9999953
2	Health	1	5889	108	163224	0.9811250	0.9999953
3	Meat and Carcass	3	18258	108	163224	0.9997109	0.9999953
4	Milk	91	75352	108	163224	0.0000000	0.0000000
5	Production	2	19640	108	163224	0.9999848	0.9999953
6	Reproduction	7	35008	108	163224	0.9999953	0.9999953

My Image

28 GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend My Image

My Image

29 Genetic Correlation

To assess the correlation between Cortisol phenotypes and Genomic Estimated Breeding Values (GEBVs), we opt for a linear regression instead of a standard correlation test. This decision is driven by the non-normal distribution of our Cortisol phenotypes, which violates the assumptions required for traditional correlation tests.

Linear regression offers a robust alternative as it does not necessitate normality for the dependent variable. By regressing GEBVs over Cortisol, we can model the relationship between these variables. Our aim is to estimate the regression coefficient, which serves as our correlation estimate.

Due to the violation of normality assumptions for the dependent variable (Cortisol), traditional correlation tests may not provide reliable results, particularly in assessing the significance of the correlation. Therefore, alternative approaches, such as linear regression, are preferred as they do not require the same assumptions about the distribution of the dependent variable. By using linear regression, we can still assess the relationship between Cortisol and GEBVs while accommodating the non-normality of Cortisol phenotypes.

The regression model can be represented as follows: \[ y = \beta_0 + \beta_1 \times GEBV_{\text{Milk}} + \epsilon \]

Where:

\(y\) represents Cortisol phenotypes.
\(GEBV_{\text{Milk}}\) denotes the GEBV for Milk Yield.
\(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
\(\epsilon\) represents the error term capturing unexplained variability.

This approach enables us to quantify the relationship between Cortisol and GEBVs, addressing the non-normality of Cortisol phenotypes while allowing for formal hypothesis testing of the correlation’s significance.

29.1 Data preparation

The first data I received from Lucas had only 135 animals out of 260 with values the other 125 had only NA I shown this to Lucas Lucas wrote to Alisson Lucas sent me the missing animals I merged this two files

rm(list = ls())

# Load the necessary library
library(dplyr)
library(tidyverse)

cortisol_260 <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")

#This is the first dataframe with information for 135 animals and 125 NA
GEBVs1 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora.csv")
#This is the second file with information for the 125 NA animals
GEBVs2 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/elora_missing_females_2404_06_11_2024.csv")
#This are de columns we can use because we know the meaning of the acronyms
GEBVs_to_use <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebv_names_lucas_06102024_BAG.csv")


sum(is.na(GEBVs1$MILK))
GEBVs1<- GEBVs1[which(is.na(GEBVs1[,"DHI_BARN_NAME"]) == F),]

sum(!is.na(GEBVs2$MILK))
GEBVs2<- GEBVs2[which(is.na(GEBVs2[,"DHI_BARN_NAME"]) == F),]

print(GEBVs1$DHI_BARN_NAME)
print(GEBVs2$DHI_BARN_NAME)

# Making the two dataframes with the same columns
# Remove elora_id and international_id from GEBVs1
GEBVs1 <- GEBVs1 %>% select(-elora_id, -international_id)

# Remove ANIMAL_ID from GEBVs2
GEBVs2 <- GEBVs2 %>% select(-ANIMAL_ID)

# Check if the two dataframes have the same columns
have_same_columns <- all(names(GEBVs1) == names(GEBVs2))

if (have_same_columns) {
  print("The dataframes have the same columns.")
} else {
  print("The dataframes do not have the same columns.")
}


# Check if the column names are in the same order
same_order <- identical(names(GEBVs1), names(GEBVs2))

if (same_order) {
  print("The columns are in the same order.")
} else {
  print("The columns are not in the same order.")
}

GEBVs_combined <- rbind(GEBVs1, GEBVs2)

# Sort the columns
sorted_cortisol_260 <- sort(cortisol_260$ID)
sorted_GEBVs_combined <- sort(GEBVs_combined$DHI_BARN_NAME)

# Check if the sorted columns have the same values
identical(sorted_cortisol_260, sorted_GEBVs_combined)

# Create a duplicate of the column 'DHI_BARN_NAME' and name it 'elora_id'
GEBVs_combined$elora_id <- GEBVs_combined$DHI_BARN_NAME

# Assuming GEBVs_combined is your data frame
GEBVs_combined <- GEBVs_combined %>%
  select(elora_id, DHI_BARN_NAME, everything())

write.csv(GEBVs_combined, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora_complete.csv")

# Merging the dataframe with Cortisol values, with the dataframe with GEBVs values
Merg_Cort_GEBVs <- merge(cortisol_260, GEBVs_combined, by.x = "ID", by.y = "elora_id")

write.csv(Merg_Cort_GEBVs, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/Merged_Cortisol_GEBVs.csv")

#Opening the file with the GEBVs columns to use
Columns_to_use <- readLines("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/traits_to_use.txt")

colnames(Merg_Cort_GEBVs)[405] <- "IDD"

data <- select(Merg_Cort_GEBVs, ID, T4Cortisol, BIRTH_YEAR, all_of(Columns_to_use))

# The data below has the the 55 GEBVs + Cortisol data + Birth Year
write.csv(data, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits.csv")

samp_date2 <- read.csv("/home/bambrozi/2_CORTISOL/Data/Elora animal_ids_kl_sampling_date.csv")

# Convert Sampling_date to Date using as.Date
samp_date$Sampling_date <- as.Date(samp_date$Sampling_date, format = "%m/%d/%Y")

table(samp_date$Sampling_date)

samp_date <- select(samp_date, Elora_id, Sampling_date)

# Check if data$ID and samp_dates$elora_id are identical in values and order
identical(data$ID, samp_date$Elora_id)

data_final <- merge(data, samp_date, by.x="ID", by.y="Elora_id")

data_final <- data_final %>%
  select(ID, T4Cortisol, BIRTH_YEAR, Sampling_date, everything())

# The data below has the the 55 GEBVs + Cortisol data + Birth Year + Sampling data
write.csv(data_final, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv")

ps. I double checked by hand the select and merge process against the original tables received and is everything ok.

29.2 Correlations - Linear Regression

29.2.1 Test with one trait

# Fit the linear regression model
model <- lm(T4Cortisol ~ MILK, data = data)

# Summarize the model to get the regression coefficients and statistical summary
model_summary <- summary(model)

# Extract the desired statistics
multiple_r_squared <- model_summary$r.squared
adjusted_r_squared <- model_summary$adj.r.squared
f_statistic <- model_summary$fstatistic[1]
p_value <- pf(model_summary$fstatistic[1], model_summary$fstatistic[2], model_summary$fstatistic[3], lower.tail = FALSE)

# Combine the statistics into a data frame
results <- data.frame(
  Multiple_R_Squared = multiple_r_squared,
  Adjusted_R_Squared = adjusted_r_squared,
  F_Statistic = f_statistic,
  P_Value = p_value
)

# Save the results to a CSV file
write.csv(results, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/regression_summary_milk.csv", row.names = FALSE)

# Plot the linear regression between T4Cortisol and MILK
ggplot(data, aes(x = MILK, y = T4Cortisol)) +
  geom_point() +  # Add points
  geom_smooth(method = "lm", se = FALSE, color = "blue") +  # Add linear regression line
  labs(title = "Linear Regression of T4Cortisol on MILK",
       x = "MILK",
       y = "T4Cortisol") +
  theme_minimal()

Plot linear regression Cortisol vs. Milk GEBV My Image

Summary statistics

Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value
5.2e-05	-0.0038237	0.0134257	0.9078463

29.2.2 Loop for all traits

As we have 54 GEBVs to fit a linear regression we designed a loop:

# Initialize a list to store the results
results_list <- list()

# Loop through columns 4 to ncol(data) for the GEBVs
for (i in 4:ncol(data)) {
  trait_name <- colnames(data)[i]
  
  # Fit the linear regression model
  model <- lm(data[[2]] ~ data[[i]], data = data)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 3]] <- result
}

# Combine all results into a single data frame
results_df_0 <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/regression_summary_all_traits.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value	Coefficient	Coefficient_P_Value
BMR	0.0400473	0.0363266	10.7632421	0.0011781	21.1128882	0.0011781
CO	0.0280949	0.0243278	7.4580136	0.0067510	-13.2769834	0.0067510
MSL	0.0198445	0.0160455	5.2235464	0.0230946	-16.9454262	0.0230946
CTFS	0.0185227	0.0147186	4.8690531	0.0282237	15.8469990	0.0282237
UT	0.0166425	0.0128310	4.3664218	0.0376334	-15.6741466	0.0376334
MS	0.0128559	0.0090298	3.3600230	0.0679494	-14.7558389	0.0679494
CK	0.0106156	0.0067808	2.7682148	0.0973674	14.6866464	0.0973674
IH	0.0101222	0.0062855	2.6382359	0.1055404	-7.3062481	0.1055404
DD	0.0100161	0.0061790	2.6103042	0.1073935	-9.5352332	0.1073935
CONF	0.0095452	0.0057062	2.4863924	0.1160600	-13.9681677	0.1160600
UD	0.0085295	0.0046866	2.2195470	0.1374945	-15.7518165	0.1374945
SU	0.0077590	0.0039131	2.0174678	0.1567056	7.3716271	0.1567056
MT	0.0076274	0.0037810	1.9829901	0.1602796	-10.6502458	0.1602796
LP	0.0074242	0.0035770	1.9297608	0.1659824	9.2112199	0.1659824
MET	0.0070642	0.0032156	1.8355367	0.1766601	-7.6934858	0.1766601
MR	0.0066755	0.0028254	1.7338637	0.1890867	-7.8752018	0.1890867
DO	0.0060791	0.0022267	1.5779921	0.2101864	9.5682702	0.2101864
DHL	0.0052895	0.0014340	1.3719419	0.2425591	-9.4618477	0.2425591
MOB	0.0047368	0.0008792	1.2279220	0.2688435	7.8139790	0.2688435
DF	0.0046402	0.0007822	1.2027541	0.2737945	7.8603320	0.2737945
FAT	0.0046204	0.0007623	1.1975980	0.2748229	1.0090961	0.2748229
SH	0.0043235	0.0004643	1.1203076	0.2908422	5.2472606	0.2908422
DS	0.0043121	0.0004528	1.1173348	0.2914818	-7.5550660	0.2914818
FOOT	0.0043005	0.0004412	1.1143288	0.2921304	26.7441735	0.2921304
FA	0.0042762	0.0004168	1.1079850	0.2935052	-8.0817790	0.2935052
PROT	0.0040026	0.0001421	1.0368190	0.3095163	1.2915168	0.3095163
WL	0.0038262	-0.0000350	0.9909414	0.3204451	5.2197508	0.3204451
MDR	0.0038096	-0.0000516	0.9866366	0.3214965	7.0545996	0.3214965
CA	0.0036379	-0.0002239	0.9420177	0.3326685	7.4603427	0.3326685
ME	0.0034176	-0.0004452	0.8847561	0.3477821	-5.0950658	0.3477821
SCS	0.0031318	-0.0007321	0.8105371	0.3688011	-4.8756313	0.3688011
FE	0.0028874	-0.0009774	0.7470985	0.3881993	-5.1774356	0.3881993
FTP	0.0027874	-0.0010778	0.7211475	0.3965550	9.8351267	0.3965550
AFS	0.0025212	-0.0013450	0.6521188	0.4201001	-5.9185799	0.4201001
UF	0.0018686	-0.0020001	0.4830008	0.4876916	6.8229142	0.4876916
ST	0.0018050	-0.0020640	0.4665276	0.4952019	-7.1228081	0.4952019
MSP	0.0014890	-0.0023812	0.3847251	0.5356327	-3.5290172	0.5356327
HL	0.0013205	-0.0025503	0.3411423	0.5596809	-4.3615962	0.5596809
SCK	0.0011411	-0.0027305	0.2947310	0.5876733	3.3934702	0.5876733
TL	0.0009008	-0.0029717	0.2326179	0.6299983	-5.0172076	0.6299983
LOC	0.0008542	-0.0030185	0.2205721	0.6390011	-3.5628830	0.6390011
DA	0.0006826	-0.0031907	0.1762328	0.6749803	3.5280623	0.6749803
CW	0.0005859	-0.0032878	0.1512479	0.6976664	-2.7721164	0.6976664
BCS	0.0003594	-0.0035151	0.0927672	0.7609338	2.1412729	0.7609338
RUM	0.0003149	-0.0035598	0.0812707	0.7758114	-1.6952340	0.7758114
TU	0.0002204	-0.0036547	0.0568793	0.8116875	1.0400512	0.8116875
FL	0.0001793	-0.0036960	0.0462618	0.8298705	1.5296709	0.8298705
BQ	0.0001697	-0.0037056	0.0437957	0.8343993	-1.5434870	0.8343993
HHE	0.0000521	-0.0038236	0.0134478	0.9077707	0.6475671	0.9077707
MILK	0.0000520	-0.0038237	0.0134257	0.9078463	0.0042447	0.9078463
HH	0.0000509	-0.0038249	0.0131340	0.9088482	-0.6606061	0.9088482
BD	0.0000293	-0.0038466	0.0075535	0.9308097	-0.6433874	0.9308097
DCA	0.0000046	-0.0038713	0.0011932	0.9724707	-0.2685932	0.9724707
FEED	0.0000001	-0.0038759	0.0000152	0.9968903	-0.0190613	0.9968903

Five out 54 traits GEBVs presented significant correlations with Cortisol:

BMR = Body Maintenance Requirements
CO = Cystic ovaries
MSL = Median Suspensory Ligament
CTFS = Calving to First Service
UT = Udder Texture

29.2.3 Adding BIRTH_YEAR

29.2.3.1 Model Description

The regression model added the BY is shown bellow:

\[ y = \beta_0 + \beta_1 \times GEBV_{\text{Trait}} + BIRTH\_YEAR + \epsilon \]

Where:

\(y\) represents Cortisol phenotypes.
\(GEBV_{\text{Trait}}\) denotes the GEBV for the specific trait (e.g., Milk Yield).
\(BIRTH\_YEAR\) is the birth year of the subjects, included as a factor.
\(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
\(\epsilon\) represents the error term capturing unexplained variability.

The BIRTH_YEAR variable is converted to a factor to account for the categorical nature of birth years.

# Convert BIRTH_YEAR to a factor and rename
data$BIRTH_YEAR <- as.factor(data$BIRTH_YEAR)

# Initialize a list to store the results
results_list <- list()

# Loop through columns 3 to ncol(data) for the GEBVs
for (i in 4:ncol(data)) {
  trait_name <- colnames(data)[i]
  
  # Fit the linear regression model with BIRTH_YEAR as an additional predictor
  model <- lm(data[[2]] ~ data[[i]] + BIRTH_YEAR, data = data)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 2]] <- result
}

# Combine all results into a single data frame
results_df <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/regression_summary_all_traits_BY.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs adding BIRTY_YEAR

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value	Coefficient	Coefficient_P_Value
BMR	0.0881915	0.0738886	6.165997	0.0000945	19.1527245	0.0028131
CO	0.0803421	0.0659161	5.569252	0.0002590	-12.5790675	0.0094097
MSL	0.0734487	0.0589145	5.053527	0.0006186	-16.2613977	0.0277614
IH	0.0719150	0.0573568	4.939831	0.0007494	-9.3449051	0.0354801
CTFS	0.0705440	0.0559642	4.838504	0.0008891	14.3231800	0.0442704
UT	0.0704128	0.0558310	4.828827	0.0009037	-14.8606501	0.0452229
MS	0.0686230	0.0540132	4.697043	0.0011284	-14.8462730	0.0606040
CK	0.0670600	0.0524256	4.582365	0.0013689	15.4450943	0.0785803
PROT	0.0658116	0.0511577	4.491053	0.0015963	2.1108214	0.0970292
DD	0.0638377	0.0491528	4.347166	0.0020332	-8.6326827	0.1365405
FAT	0.0628176	0.0481167	4.273045	0.0023028	1.2796392	0.1637385
SU	0.0623970	0.0476895	4.242529	0.0024239	7.0165690	0.1767005
CONF	0.0621537	0.0474424	4.224892	0.0024968	-11.6231634	0.1847326
MET	0.0618359	0.0471196	4.201861	0.0025952	-7.2376432	0.1958719
MR	0.0617517	0.0470341	4.195767	0.0026218	-7.5588944	0.1989504
FOOT	0.0614269	0.0467042	4.172251	0.0027273	31.1509094	0.2113774
MOB	0.0613973	0.0466741	4.170111	0.0027372	8.6217376	0.2125534
LP	0.0613481	0.0466241	4.166549	0.0027536	8.0864569	0.2145285
FTP	0.0608284	0.0460963	4.128971	0.0029327	13.5010333	0.2367661
DO	0.0607904	0.0460577	4.126220	0.0029463	8.8960747	0.2385010
ME	0.0607825	0.0460496	4.125648	0.0029491	-6.2944604	0.2388636
UD	0.0606216	0.0458862	4.114024	0.0030071	-12.1091142	0.2463830
FA	0.0605549	0.0458185	4.109209	0.0030315	-8.6648464	0.2495819
MT	0.0601784	0.0454361	4.082026	0.0031728	-8.2421107	0.2686411
CA	0.0599002	0.0451535	4.061948	0.0032814	8.3011337	0.2838987
DF	0.0598369	0.0450892	4.057381	0.0033066	7.4854663	0.2875213
DHL	0.0595211	0.0447684	4.034611	0.0034352	-8.1319072	0.3064961
SH	0.0586677	0.0439017	3.973163	0.0038074	4.4345159	0.3666760
MILK	0.0585384	0.0437704	3.963861	0.0038671	0.0328321	0.3771595
MDR	0.0584166	0.0436466	3.955101	0.0039243	6.0467308	0.3874240
ST	0.0578881	0.0431099	3.917120	0.0041817	-7.9781463	0.4369789
SCS	0.0576964	0.0429152	3.903356	0.0042790	-3.9614430	0.4573264
AFS	0.0575918	0.0428089	3.895845	0.0043331	-5.2064660	0.4690656
MSP	0.0575087	0.0427245	3.889880	0.0043765	-3.9533312	0.4787399
DS	0.0573283	0.0425413	3.876936	0.0044723	-4.7841746	0.5009026
WL	0.0572605	0.0424724	3.872073	0.0045088	3.4479700	0.5096796
TL	0.0572547	0.0424665	3.871655	0.0045119	-6.7563254	0.5104479
FE	0.0572054	0.0424165	3.868123	0.0045386	-3.8209975	0.5170084
DA	0.0566761	0.0418789	3.830184	0.0048356	4.3714958	0.5986609
HHE	0.0565353	0.0417358	3.820095	0.0049178	2.6941677	0.6249126
HL	0.0563327	0.0415301	3.805592	0.0050384	-3.1669335	0.6676242
LOC	0.0562600	0.0414563	3.800388	0.0050823	-3.0195770	0.6847865
FL	0.0562527	0.0414489	3.799865	0.0050868	2.8115963	0.6865743
SCK	0.0560192	0.0412117	3.783153	0.0052307	1.9379506	0.7520160
CW	0.0559471	0.0411384	3.777995	0.0052759	-1.9950607	0.7767448
FEED	0.0559443	0.0411356	3.777797	0.0052777	-1.3559978	0.7777588
BQ	0.0559195	0.0411104	3.776021	0.0052933	-1.9570851	0.7870649
TU	0.0558834	0.0410737	3.773438	0.0053162	1.0731908	0.8014578
UF	0.0558449	0.0410347	3.770689	0.0053406	2.2354069	0.8181403
DCA	0.0557115	0.0408991	3.761148	0.0054263	-1.0069789	0.8965483
BD	0.0557011	0.0408885	3.760401	0.0054331	0.8702554	0.9055126
BCS	0.0556786	0.0408657	3.758795	0.0054477	0.6193741	0.9285710
RUM	0.0556595	0.0408464	3.757433	0.0054601	-0.3154634	0.9570449
HH	0.0556561	0.0408428	3.757185	0.0054623	-0.2539972	0.9646162

Fitting Birth_Year to the model made one more trait significativally correlated with cortisol and enhanced the proportio of variation explained by the model:

BMR = Body Maintenance Requirements
CO = Cystic ovaries
MSL = Median Suspensory Ligament
CTFS = Calving to First Service
UT = Udder Texture
IH = Interdigital Hyperplasia

Below we can check the improvement in the percentage of variation in Cortisol explained by the Trait_GEBVs + Birth_Year.

CORR_BY <- read.csv("/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/regression_summary_all_traits_BY.csv")

CORR_MIN <- read.csv("/home/bambrozi/2_CORTISOL/Correlation/Results/regression_summary_all_traits.csv")

CORR_BY <- CORR_BY[, c("Trait", "Adjusted_R_Squared"), drop = FALSE]

CORR_MIN <- CORR_MIN[, c("Trait", "Adjusted_R_Squared"), drop = FALSE]

colnames(CORR_BY)[colnames(CORR_BY) == "Adjusted_R_Squared"] <- "Adjusted_R_Squared_BY"

colnames(CORR_MIN)[colnames(CORR_MIN) == "Adjusted_R_Squared"] <- "Adjusted_R_Squared_MIN"

Corr_R2_comp <- merge(CORR_MIN, CORR_BY, by.x = "Trait", by.y = "Trait")

Corr_R2_comp$Absolute_Change <- Corr_R2_comp$Adjusted_R_Squared_BY - Corr_R2_comp$Adjusted_R_Squared_MIN

write.csv(Corr_R2_comp, "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/absolute_improv_r2.csv")

X	Trait	Adjusted_R_Squared_Model_MIN	Adjusted_R_Squared_MIN.BY	Absolute_Change
1	AFS	-0.0013450	0.0428089	0.0441539
2	BCS	-0.0035151	0.0408657	0.0443809
3	BD	-0.0038466	0.0408885	0.0447351
4	BMR	0.0363266	0.0738886	0.0375621
5	BQ	-0.0037056	0.0411104	0.0448160
6	CA	-0.0002239	0.0451535	0.0453774
7	CK	0.0067808	0.0524256	0.0456448
8	CO	0.0243278	0.0659161	0.0415882
9	CONF	0.0057062	0.0474424	0.0417362
10	CTFS	0.0147186	0.0559642	0.0412457
11	CW	-0.0032878	0.0411384	0.0444262
12	DA	-0.0031907	0.0418789	0.0450696
13	DCA	-0.0038713	0.0408991	0.0447705
14	DD	0.0061790	0.0491528	0.0429738
15	DF	0.0007822	0.0450892	0.0443070
16	DHL	0.0014340	0.0447684	0.0433344
17	DO	0.0022267	0.0460577	0.0438310
18	DS	0.0004528	0.0425413	0.0420884
19	FA	0.0004168	0.0458185	0.0454018
20	FAT	0.0007623	0.0481167	0.0473544
21	FE	-0.0009774	0.0424165	0.0433939
22	FEED	-0.0038759	0.0411356	0.0450115
23	FL	-0.0036960	0.0414489	0.0451449
24	FOOT	0.0004412	0.0467042	0.0462629
25	FTP	-0.0010778	0.0460963	0.0471741
26	HH	-0.0038249	0.0408428	0.0446677
27	HHE	-0.0038236	0.0417358	0.0455595
28	HL	-0.0025503	0.0415301	0.0440805
29	IH	0.0062855	0.0573568	0.0510713
30	LOC	-0.0030185	0.0414563	0.0444747
31	LP	0.0035770	0.0466241	0.0430471
32	MDR	-0.0000516	0.0436466	0.0436982
33	ME	-0.0004452	0.0460496	0.0464948
34	MET	0.0032156	0.0471196	0.0439039
35	MILK	-0.0038237	0.0437704	0.0475941
36	MOB	0.0008792	0.0466741	0.0457949
37	MR	0.0028254	0.0470341	0.0442086
38	MS	0.0090298	0.0540132	0.0449834
39	MSL	0.0160455	0.0589145	0.0428690
40	MSP	-0.0023812	0.0427245	0.0451057
41	MT	0.0037810	0.0454361	0.0416552
42	PROT	0.0001421	0.0511577	0.0510155
43	RUM	-0.0035598	0.0408464	0.0444062
44	SCK	-0.0027305	0.0412117	0.0439421
45	SCS	-0.0007321	0.0429152	0.0436472
46	SH	0.0004643	0.0439017	0.0434374
47	ST	-0.0020640	0.0431099	0.0451738
48	SU	0.0039131	0.0476895	0.0437764
49	TL	-0.0029717	0.0424665	0.0454382
50	TU	-0.0036547	0.0410737	0.0447284
51	UD	0.0046866	0.0458862	0.0411996
52	UF	-0.0020001	0.0410347	0.0430348
53	UT	0.0128310	0.0558310	0.0430000
54	WL	-0.0000350	0.0424724	0.0425074

Adjusted_R_Squared_Model_MIN = Model without Birth Year
Adjusted_R_Squared_MIN.BY = Model adding Birth Year

29.2.4 Adding BIRTH_YEAR and SAMPLING DATE

The regression model added the BY and SAMPLING DATE is shown bellow:

\[ y = \beta_0 + \beta_1 \times GEBV_{\text{Trait}} + BIRTH\_YEAR + SAMPLING\_DATE + \epsilon \]

Where:

\(y\) represents Cortisol phenotypes.
\(GEBV_{\text{Trait}}\) denotes the GEBV for the specific trait (e.g., Milk Yield).
\(BIRTH\_YEAR\) is the birth year of the subjects, included as a factor.
\(SAMPLING\_DATE\) is the cortisol sampling date for the subjects, included as a factor.
\(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
\(\epsilon\) represents the error term capturing unexplained variability.

The SAMPLING_DATE variable is also converted to a factor to account for the categorical nature of sampling date.

# Convert BIRTH_YEAR to a factor and rename
data_final$BIRTH_YEAR <- as.factor(data_final$BIRTH_YEAR)

# Convert Sampling_data to a factor and rename
data_final$Sampling_date <- as.factor(data_final$Sampling_date)

# Initialize a list to store the results
results_list <- list()

# Loop through columns 3 to ncol(data) for the GEBVs
for (i in 5:ncol(data_final)) {
  trait_name <- colnames(data_final)[i]
  
  # Fit the linear regression model with BIRTH_YEAR as an additional predictor
  model <- lm(data_final[[2]] ~ data_final[[i]] + data_final$BIRTH_YEAR + data_final$Sampling_date , data = data_final)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 2]] <- result
}

# Combine all results into a single data frame
results_df <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/regression_summary_all_traits_BY.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs adding BIRTY_YEAR and SAMPLING_DATE

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	Coefficient	Coefficient_P_Value
CO	0.3913617	0.3257757	5.967151	-11.1849879	0.0099861
BMR	0.3899307	0.3241904	5.931386	14.4612444	0.0135665
LP	0.3858330	0.3196512	5.829896	12.2619950	0.0330355
MILK	0.3850052	0.3187343	5.809559	0.0671160	0.0396652
PROT	0.3841834	0.3178238	5.789422	2.2166395	0.0476301
UT	0.3822859	0.3157218	5.743130	-12.1238483	0.0731558
CK	0.3801906	0.3134008	5.692345	12.7333709	0.1192726
HHE	0.3795579	0.3126999	5.677076	7.4003505	0.1388525
MSP	0.3793006	0.3124149	5.670876	-7.2388952	0.1478152
DA	0.3791391	0.3122360	5.666989	10.8037510	0.1537699
TU	0.3785722	0.3116080	5.653351	-5.2064639	0.1769405
MSL	0.3777098	0.3106526	5.632656	-8.6230634	0.2203509
BQ	0.3775224	0.3104450	5.628166	-7.7284891	0.2313766
IH	0.3772456	0.3101385	5.621542	-4.7354916	0.2488963
ST	0.3767980	0.3096426	5.610839	-9.9721644	0.2808121
LOC	0.3766890	0.3095218	5.608233	-7.2367609	0.2893509
FAT	0.3765584	0.3093772	5.605116	0.8520581	0.3000062
UD	0.3763515	0.3091480	5.600177	-9.3297651	0.3179533
CA	0.3757417	0.3084725	5.585641	6.0756898	0.3798799
MS	0.3755038	0.3082090	5.579979	-6.0375749	0.4085912
SCK	0.3754363	0.3081342	5.578372	-4.4161473	0.4173165
SCS	0.3754033	0.3080976	5.577587	3.9080681	0.4216769
IDD	0.3752677	0.3079474	5.574362	3.7029601	0.4403519
FOOT	0.3751830	0.3078536	5.572349	17.3309360	0.4526548
TL	0.3750803	0.3077398	5.569908	-6.6585204	0.4683140
MET	0.3749055	0.3075462	5.565755	-3.4014191	0.4970656
CTFS	0.3747600	0.3073850	5.562300	4.0557716	0.5233387
CONF	0.3746137	0.3072229	5.558828	-4.7896643	0.5523352
FE	0.3745078	0.3071056	5.556316	-2.9375586	0.5752627
HL	0.3743988	0.3069849	5.553731	3.4274416	0.6009102
FA	0.3742870	0.3068610	5.551080	-3.2821641	0.6298652
DD	0.3742836	0.3068573	5.551001	2.5402141	0.6307730
MOB	0.3742556	0.3068263	5.550337	3.0592146	0.6385442
FTP	0.3742359	0.3068045	5.549870	4.8583743	0.6441419
BCS	0.3741681	0.3067293	5.548263	2.6692352	0.6643615
HH	0.3740753	0.3066266	5.546066	2.0230249	0.6947785
DF	0.3740392	0.3065865	5.545209	2.3681806	0.7076996
FL	0.3739824	0.3065237	5.543865	-2.1711035	0.7294613
UF	0.3739605	0.3064994	5.543347	2.8880683	0.7384413
MDR	0.3738855	0.3064163	5.541571	1.8495373	0.7722558
WL	0.3738548	0.3063822	5.540843	1.2643028	0.7878801
AFS	0.3738293	0.3063540	5.540239	1.5814380	0.8018670
RUM	0.3738023	0.3063241	5.539601	-1.2574561	0.8179113
SH	0.3738015	0.3063233	5.539583	1.0342843	0.8184040
BD	0.3736958	0.3062061	5.537080	0.7613199	0.9071017
MR	0.3736957	0.3062060	5.537078	0.6291752	0.9071898
SU	0.3736871	0.3061965	5.536876	0.4745538	0.9186634
FEED	0.3736818	0.3061907	5.536751	0.3944746	0.9266753
DHL	0.3736774	0.3061858	5.536647	-0.5919776	0.9340753
DO	0.3736757	0.3061838	5.536604	0.5220723	0.9373261
DS	0.3736695	0.3061769	5.536458	0.3979132	0.9502668
CW	0.3736676	0.3061749	5.536414	0.3562368	0.9547808
ME	0.3736658	0.3061729	5.536370	-0.2401976	0.9599012
MT	0.3736595	0.3061659	5.536223	-0.0976386	0.9882642
DCA	0.3736593	0.3061657	5.536217	0.0802555	0.9906432

Fitting Birth_Year and Sampling_date to the model these are the traits with significant correlation:

CO = Cystic ovaries
BMR = Body Maintenance Requirements
LP = Lactation persistency
MILK = Milk yield
PROT = Protein yield
UT = Udder Texture

30 Heritability estimation - BLUPF90

30.1 Files preparation

Preparing files to run Variance components estimation using REML with AI (Average Information) algorithm.

First you need to create a directory in your home directory, prepare and save the following files in:

Phenotype and Fixed effects file
Pedigree file
Genotype file
BlupF90+ executable file
RenumF90 executable file
Parameter file

30.2 Running renumF90 and BlupF90+

Go to the server you wanna run this analysis, for instance, grand
Now go to the directory you have created to run this analysis where that set of files are placed.

ssh grand

Make the renumF90 and BlupF90+ files executables

chmod +x renumf90
chmod +x blupf90+

Run renumF90

./renumf90

When you run the code above, it will as you the name of your parameter card.

renumF90 will generate a new parameter card called renf90.par

Run blupf90+

./blupf90+

blupf90+ will ask you for parameter’s card name, now you should provide with the new one renf90.par

blupF90+ will generate the blupf90.log file with the results.

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Now you should update you renf90.par file with these informations from the .log file

Copy Residual Variance from blupf90.log and will paste on renf90.par RANDOM_RESIDUAL_VALUES Copy Genetic variance for effect x from blupf90.log and will paste on renf90.par (CO) VARIANCE

2nd blupf90+ run

./blupf90+

blupf90+ will ask you for parameter’s card name, now you should provide with the UPDATED renf90.par

If the Residual Variance and Genetic variance for effect x didn’t change in your blupf90.log the analysis ended, but if this value vary, you should update again the renf90.par and run again blupf90+ until this values don’t change more.

30.3 Running renumF90 and BlupF90+ adding GENOTYPES

The previous analysis considered only the pedigree, but now we can insert the genotype information. To perform this you need a new diretory called Blup_Genomic inside your previously created directory.

Now you need add the reference for your genotype file in your previous parameter file renum.par and save in this new sub-directory.

The highlighted text show the added part.

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Go to the sub-diretory

Note that as you are in the subdirectory, but your phenotype and fixed effect, pedigree and genotype files are still in the previous directory you need to add the highlighted part to inform the correct location

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To run the renumf90 and blupf90+ you also need to add ../ to correct specify the location. Run renumF90

./../renumf90

Run blupf90+

./../blupf90+

The steps for run, update parameter card, re-run are the same.

30.4 Results

We have 2 different output files

Variance components: blupf90.log

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In this file we can find the heritabilit (SD) and for instance the convergence (similarity)

Solutions: solutions.orig
In this file we will find the solutions (results) for each effect

In our example:

EFFECT 1: Birth Year, has 4 levels (2018, 2019, 2020 and 2021), and the solution that for this fixed effect is how much each level add.
EFFECT 2: Sampling date, has 23 levels, and the solutions
EFFECT 3: Animal random effect, has one for each animal and it is the EBV or GEBV.

31 Storage 1

32 GWAS - EXTREME PHENO - WITH BY and SD

32.1 Data preparation

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

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pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv", header = T)
ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)


# Create an matrix with fixed effects with only those animals which also have phenotype and genotype
data_final$cdn_id <- ids_eq$cdn_id[match(data_final$ID, ids_eq$elora_id)]
fixeff <- data_final[,c("ID", "BIRTH_YEAR", "Sampling_date", "cdn_id")]
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id, ]
fixeff$FID <- "HO"
fixeff <- fixeff[, c("FID", "cdn_id", "BIRTH_YEAR", "Sampling_date")]

# Now we are gona remove the intermediary animals from pheno object
pheno$Categorical <- ifelse(pheno$cortisol >= 956, "H", 
                           ifelse(pheno$cortisol <= 190.8, "L", "M"))
table(pheno$Categorical)
pheno <- pheno[pheno$Categorical != "M", ]
pheno <- pheno[, c("FID", "cdn_id", "cortisol")]

# Now we are going to remove from fixeff the animals which are not in pheno
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id,]

#Checking if match the animals and order
identical(fixeff$cdn_id, pheno$cdn_id)

#Creating a file with animals to keep in the genotype file, we will use it on Plink
to_keep_geno <- pheno[, c("FID", "cdn_id")]

write.table(fixeff, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt", quote = F, row.names = F, col.names = T)
write.table(pheno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt", quote = F, row.names = F, col.names = T)
write.table(to_keep_geno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt", quote = F, row.names = F, col.names = F)

We ended up with
H (Hight) = 34 animals
L (Low) = 37 animals Total = 71 animals

On Plink we will remove all individuals from genotype files that are classified as Medium, keeping only the High and Low

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
KEEP=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt

plink2 --bfile ${DATA} --keep ${KEEP} --chr-set 29 --make-bed --out ${RESULT}

32.2 GWAS on GCTA

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

After ran the GWAS above I got the following message from the GCTA:

Error: analysis stopped because more than half of the variance components are constrained. The result would be unreliable. You may have a try of the option –reml-no-constrain.

Than a ran this new code with NO CONSTRAIN

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result_NO_CONSTRAIN
PHENO=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --reml-no-constrain \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

32.2.1 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result_NO_CONSTRAIN.loco.mlma", 
                  head=T, stringsAsFactors = F)


gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf<- -log10(0.15/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topright", col="red2", lwd=2, c("Bonferroni corr. for ind. segments"), bty="n")


library(tidyverse)

SNP_sig_bonf_ind_seg <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf_ind_seg, file = "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/SNP_sig15_Bonf_ind_seg.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(SNP_sig_bonf_ind_seg, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/SNP_sig15_Bonf_ind_seg.txt")

The SNPs below are the significant after the correction above

X	Chr	SNP	bp	A1	A2	Freq	b	se	p	logP
1	3	BTB-00148619	105840426	A	C	0.321429	410.739	104.530	0.0000852	4.069736
2	3	ARS-BFGL-NGS-118207	110866523	A	C	0.421429	402.730	103.634	0.0001019	3.991928
3	22	ARS-BFGL-NGS-23411	5134078	A	C	0.214286	415.637	109.100	0.0001391	3.856526

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Bellow we inserted the rsID searched at SNPCHIMP

X	Chr	rsID	SNP	bp	A1	A2	Freq	b	se	p	logP
1	3	rs43357861	BTB-00148619	105840426	A	C	0.321429	410.739	104.530	0.0000852	4.069736
2	3	rs110081798	ARS-BFGL-NGS-118207	110866523	A	C	0.421429	402.730	103.634	0.0001019	3.991928
3	22	rs110031217	ARS-BFGL-NGS-23411	5134078	A	C	0.214286	415.637	109.100	0.0001391	3.856526

32.2.2 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.csv(file = "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/SNP_sig15_Bonf_ind_seg.csv")

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_genes_extr_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_genes_extr_15.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_qtl_extr_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_qtl_extr_15.csv")

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	3	BTB-00148619	105840426	3	105807211	105810027	2817	+	ENSBTAG00000020622	COL9A2	lncRNA
2	3	BTB-00148619	105840426	3	105824376	105867157	42782	-	ENSBTAG00000044178	ZMPSTE24	protein_coding
3	3	BTB-00148619	105840426	3	105884724	105887360	2637	-	ENSBTAG00000019698	TMCO2	protein_coding
4	3	ARS-BFGL-NGS-118207	110866523	3	110769753	110827938	58186	+	ENSBTAG00000013867	DLGAP3	protein_coding
5	3	ARS-BFGL-NGS-118207	110866523	3	110835276	110839536	4261	+	ENSBTAG00000014235	SMIM12	protein_coding
6	3	ARS-BFGL-NGS-118207	110866523	3	110888198	110890904	2707	-	ENSBTAG00000013881	GJA4	protein_coding
7	3	ARS-BFGL-NGS-118207	110866523	3	110900183	110905849	5667	-	ENSBTAG00000012584	GJB3	protein_coding
8	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	3	BTB-00148619	105840426	Production	105835011	105835015	287197
2	3	ARS-BFGL-NGS-118207	110866523	Meat_and_Carcass	110866521	110866525	152332
3	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
4	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
5	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601

32.2.2.1 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_enrich_qtl_genome_name_extr_15.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/EXTREM_PHENO_BY_SD/out_enrich_qtl_genome_type_extr_15.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
2	Muscle carnosine content	3	67	5	163224	0.0000000	0.0000000	Meat and Carcass
3	Polyunsaturated fatty acid content	1	28	5	163224	0.0008574	0.0012861	Meat and Carcass
1	Body depth	1	616	5	163224	0.0187281	0.0187281	Production

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Meat and Carcass	4	18258	5	163224	0.0007125	0.0014251
2	Production	1	19640	5	163224	0.4732476	0.4732476

My Image

32.2.3 gPROFILER online

From the online version of GPROFILER i got the following results.

Legend My Image

My Image

\[ H_3 = \begin{pmatrix} 14 & 15 \\ 16 & 17 \\ 18 & 19 \\ 20 & 21 \end{pmatrix} \]

Read_me

Galindo, B. A

2024-05-22

1 Raw data

2 Continuous Phenotype

2.1 Categorical Phenotype

2.2 Removing “outliers”

3 GENOTYPES

3.1 Transforming the Genotypes in 012 format to plink format

4 GWAS

4.1 Checking with Phenotyped Animals also have Genotype

4.2 Generating a Phenotype file

4.3 Adjusting the SNP_map to .map

4.4 Generating the bfiles

5 Quality Control

6 KING

7 PCA

7.1 PCA Plot

8 GWAS on GCTA

9 5%

9.1 Manhattan Plot - Bonferroni

9.2 FDR

9.3 Corrected Bonferroni for genome independent segments

10 qqPlot

11 VEP - Variant Effect Prediction

12 GALLO

12.1 QTL annotation on GALLO

12.2 QTL enrichment on GALLO

13 GPROFILER

13.1 GPROFILER ON-LINE

13.2 Terms distribution Pie Chart

14 Haplotype Blocks

14.1 BLOCK 10 in Chr 26

15 5 - 15%

15.1 Corrected Bonferroni for genome independent segments

16 qqPlot

17 GWAS on GCTA - considering BIRTH_YEAR and SAMPLING_DATE

17.1 Corrected Bonferroni for genome independent segments

17.1.1 Option 2: Manhattan Plot

18 VEP - Variant Effect Prediction

19 VEP - With Birth_year and Sampling_date

20 GALLO

20.1 QTL annotation on GALLO

20.2 QTL enrichment on GALLO

21 GALLO - with Birth_Year and Sampling_Date

21.1 QTL annotation on GALLO

21.2 QTL enrichment on GALLO

22 GPROFILER ON-LINE with BY and SD

23 Haplotype Blocks

24 GWAS - EXTREME PHENO - WITH BY and SD

24.1 Data preparation

24.2 GWAS on GCTA

24.3 BLUPF90+ GWAS

24.3.1 Files preparation

24.3.1.1 Phenotype and Fixed effects file

24.3.1.2 Pedigree file

24.3.1.3 Genotype file

24.3.2 Download the executable files

24.3.3 SNP MAP

24.3.4 Parameter file for Quality Control

24.4 Prediction of SNP marker effects based on the GEBV

24.4.1 GWAS for High and Low cortisol animals

24.4.1.1 Updating the files

24.4.1.1.1 Phenotype and Fixed Effects files

24.4.1.1.2 Genotype files

24.4.1.2 Running renum_QC.par

24.4.1.3 Running renf90_DataClean.par for Quality Control

24.4.1.4 Running renf90_ssGWAS1_Ginv.par for Ginv estimation

24.4.1.5 Running renf90_ssGWAS2_SNPeff.par for GWAS

24.4.1.6 Manhattan Plots for BLUPF90 GWAS

24.4.1.6.1 Genome Independent Segment

24.4.1.6.1.1 Step 01 = Bring the SNP name to GWAS output

24.4.1.6.1.2 Step 02 = Bring the rsID to the file with SNP name.

24.4.1.7 WINDOWS

24.4.1.8 Running renf90_ssGWAS2_SNPeff_w.par for GWAS (WINDOWS)

24.5 Match Significant p-value and Windows variance bigger than 0.5%

24.6 BLUPF90+ GALLO

24.6.1 QTL enrichment on GALLO

24.7 BLUPF90+ GPROFILER ON-LINE

24.8 BLUPF90+ VEP